SLIDE SHOW GENERATION METHOD, PROGRAM, AND SLIDE SHOW GENERATION DEVICE

Abstract

The slideshow generation method in this disclosure includes: a step (step S501) for acquiring a plurality of video data pieces; a first classification step (step S502) for classifying, into a first group, first video data pieces which are from among the plurality of video data pieces and which are generated on the same day and at the same location; a second classification step (step S503) for classifying, into the first group, second video data pieces which are from among the plurality of video data pieces and which were generated during the day / time at which each of the plurality of first video data pieces belonging to the first group was generated; and a step (step S504) for selecting one or more video data pieces from among the plurality of video data pieces, each of which is a first video data piece or a second video data piece belonging to the first group; and a step (step S505) for generating slideshow data for sequentially reproducing the selected one or more video data pieces.

Description

[Technical field]

[0001] The present disclosure relates to a slideshow generating method, a program, and a slideshow generating device. [Background technology]

[0002] Patent Document 1 discloses an image playback device that sequentially switches between and displays a number of images. The image playback device stores and manages related information associated with each image, evaluates each image by referring to the related information, extracts a predetermined number of images as playback targets in descending order of evaluation based on the evaluation results, and sequentially switches between and displays the extracted multiple images. This allows the image playback device to select a predetermined number of appropriate images as playback targets and sequentially switches between and plays them, without the user having to previously select a desired playback target while checking a large number of images. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2006-279118 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a slideshow generation method and the like that allows for selection of appropriate video data to be played back. [Means for solving the problem]

[0005] The slideshow generating method of the present disclosure includes an acquisition step of acquiring multiple video data, a first classification step of classifying into a first group first video data generated on the same day and at the same place from among the multiple video data acquired in the acquisition step, a second classification step of classifying into the first group second video data, which is video data from the multiple video data acquired in the acquisition step that does not include location information indicating the place of generation, and which was generated between the dates and times when each of the multiple first video data belonging to the first group was generated, and a generation step of selecting one or more video data from the multiple video data belonging to the first group, each of which is first video data or second video data, and generating slideshow data for sequentially playing back the selected one or more video data.

[0006] Moreover, one aspect of a program in the present disclosure is a program for causing a computer to execute the slideshow generating method in the present disclosure.

[0007] In addition, the slideshow generation device of the present disclosure includes an acquisition unit that acquires multiple video data; a first classification unit that classifies into a first group first video data generated on the same day and at the same place among the multiple video data acquired by the acquisition unit; a second classification unit that classifies into the first group second video data, which is video data among the multiple video data acquired by the acquisition unit that does not include location information indicating the place of generation, and which was generated between the date and time when each of the multiple first video data belonging to the first group was generated; and a generation unit that selects one or more video data from the multiple video data included in the first group, each of which is first video data or second video data, and generates slideshow data for sequentially playing the selected one or more video data. Effect of the Invention

[0008] According to the slide show generating method and the like of the present disclosure, appropriate video data to be played back can be selected. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram showing the configuration of a slide show generating system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating a hardware configuration of the slide show generating device according to the embodiment. [Diagram 3] FIG. 3 is a block diagram showing a functional configuration of the slide show generating device according to the embodiment. [Figure 4] FIG. 4 is a flowchart showing a procedure for generating slide show data by the slide show generating device according to the embodiment. [Diagram 5] FIG. 5 is a flowchart showing a procedure for playing a slideshow by the slideshow generating device according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing a processing procedure for classifying image data in the slide show generating device according to the embodiment. [Figure 7] FIG. 7 is a flowchart showing a processing procedure of adding a classification of image data in the slide show generating device according to the embodiment. [Figure 8] FIG. 8 is a flowchart showing a processing procedure for classifying moving image data in the slideshow generating device according to the embodiment. [Figure 9] FIG. 9 is a flowchart showing a procedure for generating slide show data by the slide show generating device according to the embodiment. [Figure 10] FIG. 10 is a diagram showing a first example of classification of image data in the slide show generating device according to the embodiment. [Figure 11] FIG. 11 is a diagram showing a second example of classification of image data in the slide show generating device according to the embodiment. [Figure 12] FIG. 12 is a diagram showing a first example of classification of moving image data by the slideshow generating device in the embodiment. [Figure 13]FIG. 13 is a diagram showing a second example of classification of moving image data by the slideshow generating device in the embodiment. [Figure 14] FIG. 14 is a diagram showing a third example of classification of moving image data by the slideshow generating device in the embodiment. [Figure 15] FIG. 15 is a diagram showing a fourth example of classification of moving image data by the slideshow generating device in the embodiment. [Figure 16] FIG. 16 is a diagram showing a fifth example of classification of moving image data by the slideshow generating device in the embodiment. [Figure 17] FIG. 17 is a diagram showing a sixth example of classification of moving image data by the slideshow generating device in the embodiment. [Figure 18] FIG. 18 is a flowchart showing a processing procedure of the slide show generating device in the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] (Findings on which this disclosure is based) First, the inventors' viewpoint will be explained below.

[0011] Conventionally, there are devices that have a function of analyzing video data such as image data and video data, and generating slideshow data for sequentially playing a plurality of pieces of the video data using the analysis results. Alternatively, there are devices that generate slideshow data based on the date and time when the video data was generated, and notify the user. For example, a display device such as a monitor determines the order in which to display a plurality of pieces of video data based on the slideshow data, and sequentially displays (plays) the plurality of pieces of video data in the determined order. In other words, a display device such as a monitor displays a slideshow (also called a short movie) based on the slideshow data. These functions for generating slideshow data are intended for individuals, and are adopted, for example, in devices such as smartphones owned by individuals.

[0012] Conventionally, devices such as recorders shared by a group such as a family have a function for importing video data such as photos (image data) and videos (video data) taken with an individual's smartphone, digital camera, etc. However, even if video data is imported into the device, the situation is not such that the user can effectively use the video data.

[0013] Therefore, a function for displaying the video data captured in the device to the user as an attractive slide show will be considered.

[0014] For example, consider a case where a family goes to a sports day or on a trip. In recent years, each family member often takes photos or videos using their own smartphone or digital camera. Therefore, data of photos or videos (i.e., video data) that each family member is interested in is recorded on each smartphone or digital camera.

[0015] Slideshow data can be generated using the video data recorded on these smartphones or digital cameras, and the video data generated from each family member's perspective can be displayed on a display device, etc., allowing the whole family to look back on events that the family has participated in, such as sports days and trips, as a whole.

[0016] Furthermore, if the device generates slide show data using multiple video data, the video data can be shown to the user without the user having to generate the slide show data himself or give instructions to display the video data on a display device.

[0017] For example, a slideshow generating device, which is a device that generates this type of slideshow data, imports video data from each of multiple devices, performs image analysis (or video image analysis) on all of the imported video data, and determines the subject of the video data or the event of the video data. The slideshow generating device generates slideshow data that indicates the order in which to play the video data according to each of the determined subjects or events. For example, the slideshow generating device notifies the user when the power is turned on, and causes the display device to sequentially display the video data based on the generated slideshow data, that is, displays a slideshow. This allows the user to look back on events that the family has held together and enjoy the family together by watching the slideshow.

[0018] Here, the multiple video data may include video data for different events. In order to generate slide show data from such multiple video data, it is important to appropriately classify the video data by event and generate slide show data for each classified video data.

[0019] However, it may be difficult to appropriately classify a plurality of pieces of video data by event. Specifically, it may be difficult for a slideshow generating device to import video data generated by a plurality of devices such as smartphones and generate slideshow data based on the imported video data.

[0020] For example, video data may include (be linked to) the date and time of generation and GPS (Global Positioning System) data indicating the location of generation. However, for example, the recording method of GPS data may differ from device to device. In this case, for example, even if a slideshow generating device acquires video data from each device, classifies the video data based on the GPS data associated with the acquired video data, and attempts to generate slideshow data for each classified video data, the GPS data may not be read. In particular, for video data, there is no common standard like EXIF ​​(Exchangeable Image File Format) for image data, and the standard of data related to shooting conditions may differ depending on the device.

[0021] This makes it difficult to properly classify video data according to shooting conditions such as date, time, and location, and thus makes it difficult to generate slide show data.

[0022] In view of the above, the inventors have created the present disclosure.

[0023] Hereinafter, each embodiment will be described in detail with reference to the drawings as appropriate. However, more detailed explanation than necessary may be omitted. For example, detailed explanation of already well-known matters or duplicate explanation of substantially the same configuration may be omitted. This is to avoid the following explanation becoming unnecessarily redundant and to facilitate understanding by those skilled in the art.

[0024] Furthermore, the inventors provide the accompanying drawings and the following description so that those skilled in the art can fully understand the present disclosure, and do not intend for them to limit the subject matter described in the claims.

[0025] (Embodiment) [1. Configuration] Fig. 1 is a schematic diagram showing the configuration of a slideshow generation system 400 according to an embodiment. Fig. 2 is a diagram showing the hardware configuration of a slideshow generation device 100 according to an embodiment. Fig. 3 is a block diagram showing the functional configuration of the slideshow generation device 100 according to an embodiment.

[0026] Slide show generation system 400 is a system that classifies (groups) multiple video data into multiple groups based on predetermined conditions, generates slide show data for each group indicating the order in which the video data will be displayed (played) sequentially on a display device, and causes the display device to sequentially display the video data based on the generated slide show data (i.e., plays a slide show).

[0027] The slideshow generation system 400 includes the slideshow generation device 100, an imaging device 200, and a display device 300. Note that the smartphone 201, smartphone 202, digital camera 203, and video camera 204 shown in FIG.

[0028] The slideshow generation device 100 acquires video data from each of a smartphone 201, a smartphone 202, a digital camera 203, and a video camera 204. That is, the slideshow generation device 100 acquires video data from each of a plurality of imaging devices 200. Note that, although one imaging device 200 is illustrated in Fig. 3, the slideshow generation device 100 may acquire video data from two, three or more imaging devices 200.

[0029] In the present embodiment, video data refers to image data or moving image data.

[0030] The imaging device 200 is a device that captures an image of an object to generate and store video data, and transmits the stored video data to the slideshow generation device 100 by communicating with the slideshow generation device 100. The imaging device 200 is, for example, a smartphone 201 or 202, a digital camera 203, or a video camera 204.

[0031] The slide show generation device 100 generates slide show data indicating the playback order of a plurality of pieces of video data acquired based on a predetermined processing procedure described later. The slide show generation device 100 also causes the display device 300 to sequentially display the video data based on the playback order indicated by the generated slide show data.

[0032] The display device 300 is a device that displays images or moving images based on video data. The display device 300 is, for example, a monitor device such as a liquid crystal display or an organic EL (Electro Luminescence) display. The display device 300 sequentially displays images based on video data sequentially output from the slideshow generation device 100, for example.

[0033] The slideshow generation device 100 is a device realized by, for example, a BD (Blu-ray (registered trademark) Disc) player, a recorder, a game device, a personal computer, or the like.

[0034] As shown in FIG. 2, the slide show generation device 100 is realized by, for example, a communication IF 110a, a processor 130a, a content output IF 170a, a light receiving sensor 180a, and a recording medium 190a.

[0035] The communication IF 110a is a communication interface for communicating with the smartphone 201, the digital camera 203, etc. The communication IF 110a is realized by, for example, an antenna and a wireless communication circuit. The communication standard used when the slideshow generation device 100 communicates with the smartphone 201, the digital camera 203, etc. is not particularly limited. For example, the slideshow generation device 100 communicates with the smartphone 201, the digital camera 203, etc. based on the Bluetooth (registered trademark) standard.

[0036] The processor 130 a controls each of the components of the slide show generation device 100 .

[0037] The content output IF 170a is a connector to which a communication line is connected for communicating with a television 300a, which is an example of the display device 300. The processor 130a transmits content, such as video data and audio data stored in the recording medium 190a, to the television 300a via the content output IF 170a, thereby causing the television 300a to output video and audio of the content.

[0038] The light receiving sensor 180a is a sensor that receives an optical signal such as infrared light from a device operated by a user such as an operation console. The processor 130a executes various processes based on the optical signal received by the light receiving sensor 180a, that is, based on an instruction from the user.

[0039] The recording medium 190a is a storage device that stores information such as video data and a control program executed by the processor 130a. The recording medium 190a is, for example, a Read Only Memory (ROM), a Random Access Memory (RAM), a Hard Disk Drive (HDD), or a Solid State Drive (SSD).

[0040] Also, as shown in FIG. 3, the slideshow generation device 100 has, as its functional configuration, for example, a communication unit 110, an acquisition unit 120, a control unit 130, an estimation unit 140, a classification unit 150, a generation unit 160, an output unit 170, a decoder unit 171, a receiving unit 180, and a memory unit 190.

[0041] The communication unit 110 is a communication interface for communicating with the imaging device 200. The communication unit 110 is realized by, for example, a communication IF 110a. The communication unit 110 may perform wireless communication with the imaging device 200, or may perform wired communication via a communication line or the like. Any communication standard may be used when the communication unit 110 and the imaging device 200 communicate with each other. For example, the control unit 130 causes the video data acquired via the communication unit 110 to be stored in the storage unit 190 via the acquisition unit 120.

[0042] The acquisition unit 120 is a processing unit that acquires a plurality of pieces of video data stored in the storage unit 190 .

[0043] The control unit 130 is a processing unit that controls each of the components included in the slide show generation device 100.

[0044] The estimation unit 140 is a processing unit that estimates the type of event of the video data by performing image analysis on the video data acquired by the acquisition unit 120. An event is, for example, an event such as a trip, an athletic meet, or an outing such as shopping. The estimation unit 140 estimates whether the video data is video data generated during a trip (i.e., video data captured during a trip), video data generated at an athletic meet (i.e., video data captured at an athletic meet), or video data generated during an outing such as shopping (i.e., video data captured during an outing). For example, the estimation unit 140 calculates the possibility (probability) of what the event of the video data is, such as an 80% probability that the event of the video data is a trip and a 20% probability that the event is an athletic meet, by performing image analysis on the video data. The event estimated by the estimation unit 140 may be arbitrarily determined in advance.

[0045] The classification unit 150 is a processing unit that classifies the video data acquired by the acquisition unit 120 into a plurality of groups. For example, the classification unit 150 classifies the video data acquired by the acquisition unit 120 into each event estimated by the estimation unit 140. For example, the classification unit 150 classifies the video data into each event by classifying the video data whose event is a trip into group A and the video data whose event is an athletic meet into group B.

[0046] The classification unit 150 includes, for example, a first classification unit 151, a second classification unit 152, and a third classification unit 153.

[0047] The first classification unit 151 is a processing unit that classifies image data from among the video data. For example, the first classification unit 151 classifies, among the multiple video data acquired by the acquisition unit 120, image data (also referred to as first video data) generated at the same place on the same day into the same group (for example, the first group).

[0048] For example, the first classification unit 151 calculates the distance between the location where each of the multiple first image data among the multiple image data acquired by the acquisition unit 120 is generated and a predetermined first location. In this case, the first classification unit 151 classifies the first image data, which is the first image data for which the distance is calculated to be less than the predetermined first distance, into different groups, from the first image data, which is the first image data for which the distance is calculated to be equal to or greater than the predetermined first distance, into different groups.

[0049] The predetermined first distance is determined in advance to be, for example, 10 km, 30 km, or 100 km, but any distance may be determined.

[0050] The second classification unit 152 is a processing unit that classifies video data from among the video data. For example, the second classification unit 152 classifies into a first group the second video data (in this embodiment, video data) that is video data that does not include location information indicating a location where the data was generated, and that was generated between the dates and times when the first video data belonging to a certain group (for example, a first group) were generated. More specifically, the second classification unit 152 classifies into a first group the second video data from among the multiple video data acquired by the acquisition unit 120, the second video data that is a group including the first video data whose generation date and time is closest, and that was generated between the dates and times when the first video data belonging to the group were generated.

[0051] The location information is not particularly limited as long as it is information that can identify a position. The location information may be, for example, GPS data (coordinate data) or information that can identify a region such as "Japan" or "Osaka Prefecture."

[0052] In the following description of this embodiment, video data including location information is referred to as image data, and video data not including location information is referred to as moving image data.

[0053] For example, the video data includes identification data indicating the imaging device 200 that generated the video data. In this case, for example, when the date and time when the second video data was generated is between the dates and times when the multiple first video data belonging to the first group were generated, and when the imaging device 200 indicated by the identification data included in the second video data matches the imaging device 200 indicated by the identification data included in the multiple first video data belonging to the first group, the second classification unit 152 classifies the second video data into the first group.

[0054] For example, it is assumed that image data belonging to the first group was generated by digital camera 203, and video data was generated by digital camera 203. In this case, for example, if the video data was generated on the same day as the image data, second classification unit 152 classifies the video data into the first group. On the other hand, it is assumed that image data belonging to the first group was generated by digital camera 203, and the video data was generated by video camera 204. In this case, for example, second classification unit 152 does not classify the video data into the first group even if the video data was generated on the same day as the image data.

[0055] Here, not including location information means not including location information that can be recognized by the slideshow generation device 100. For example, even if the video data includes location information of a standard that cannot be recognized by the slideshow generation device 100, the video data will be determined not to include location information.

[0056] In addition, for example, the first classification unit 151 classifies the plurality of image data acquired by the acquisition unit 120 into the plurality of first image data belonging to the first group, the plurality of first image data being generated on at least one of a different date and a different place. The 3 video data (image data in this embodiment) is classified into the second group. That is, for example, the first classification unit 151 classifies image data that is generated on a different date or at a different place into a different group.

[0057] In this case, for example, when viewed in chronological order, the second classification unit 152 classifies the second video data into a group to which the video data generated at the date and time closest to the date and time the second video data was generated belongs, among the video data classified into either the first group or the second group, when the date and time when the second video data was generated is between the date and time when each of the multiple first video data belonging to the first group was generated and the date and time when each of the multiple third video data belonging to the second group was generated.

[0058] Alternatively, in this case, for example, when viewed chronologically, the second classification unit 152 classifies the second video data into a group to which video data belongs that has an event type that most closely matches the event type of the second video data, among the video data classified into either the first group or the second group, belongs, when the date and time when the second video data was generated is between the date and time when each of the multiple first video data belonging to the first group was generated and the date and time when each of the multiple third video data belonging to the second group was generated.

[0059] For example, suppose that the event type of the video data belonging to the first group is "athletic meet" and the event type of the video data belonging to the second group is "outing." Also, suppose that the event type of the second video data is "athletic meet" with an 80% probability and "outing" with a 20% probability. In this case, the second classification unit 152 classifies the second video data into the first group.

[0060] The third classification unit 153 is a processing unit that determines whether or not the number of pieces of video data for each group has reached a predetermined number. The slideshow generating device 100 generates slideshow data for displaying the video data in sequence on the display device 300 for each group. The time for which the slideshow is displayed on the display device 300 is required to be a certain length. Therefore, it is preferable that a certain number of pieces of video data belong to each group. Therefore, the third classification unit 153 determines whether or not the number of pieces of video data for each group has reached a predetermined number, and for groups that have not reached the predetermined number, adds video data that is likely to be related to the video data belonging to the group.

[0061] For example, the third classification unit 153 determines whether the number of the plurality of video data belonging to the first group is less than a predetermined number. When the third classification unit 153 determines that the number of the plurality of video data belonging to the first group is less than a predetermined number, it determines whether the location where the first video data belonging to the first group was generated is a location that is a predetermined distance (predetermined second distance) or more away from the predetermined location (predetermined second location).

[0062] The predetermined number may be arbitrarily determined in advance and is not particularly limited.

[0063] The predetermined second location may be arbitrarily determined in advance and is not particularly limited. The predetermined second location is, for example, the user's home.

[0064] The second distance may be arbitrarily determined in advance and is not particularly limited. The second distance may be, for example, 10 km, 30 km, or 100 km, but may be any distance.

[0065] When it is determined that the location where the first image data belonging to the first group was generated is a location that is a predetermined second distance or more away from the predetermined second location, the third classification unit 153 classifies the first image data, among the plurality of image data acquired by the acquisition unit 120, as the first image data that was generated on or after the day after the date on which each of the plurality of image data belonging to the first group was generated. Film Further, the third classification unit 153 classifies the extracted image data into Film It is determined whether or not the location where the image data was generated is the same as the location where the first video data belonging to the first group was generated.

[0066] In addition, the location being the same does not only mean, for example, that the coordinate data indicated by GPS data, which is an example of location information, is a perfect match, but also means, for example, that the area name is the same, such as "Japan" or "Osaka Prefecture", that the block is the same, or that the locations where the two first image data were generated are within a predetermined distance (a predetermined third distance) that is arbitrarily determined in advance.

[0067] The third classification unit 153 extracts Film If it is determined that the location where the first image data was generated is the same as the location where the first image data belonging to the first group was generated, Film The image data is classified into a first group.

[0068] The generating unit 160 is a processing unit that generates slide show data based on the video data acquired by the acquiring unit 120. Specifically, the generating unit 160 generates slide show data indicating the playback order of the video data belonging to each group into which the video data is classified by the classifying unit 150. For example, the generating unit 160 selects one or more pieces of video data from among a plurality of pieces of video data belonging to a first group, each of which is the first video data or the second video data, and generates slide show data for sequentially playing the selected one or more pieces of video data.

[0069] The slide show data may be data indicating the order in which a plurality of video data are to be reproduced, or may be moving image data in which a plurality of video data are joined together.

[0070] For example, the generating unit 160 arranges the multiple video data included in the first group in chronological order, starting with the video data that was generated earlier.

[0071] Here, for example, the generation unit 160 divides the total period from the date and time when the first video data in the arranged multiple video data was generated to the date and time when the last video data was generated into N equal parts (N is an integer equal to or greater than 2), and generates multiple small sets by classifying the arranged multiple video data into each of the N equal periods of the total period.

[0072] Alternatively, for example, the generating unit 160 generates a plurality of small sets by classifying the plurality of arranged video data into groups of M pieces (M is an integer of 2 or more) in the order of arrangement.

[0073] For example, the generating unit 160 selects one or more pieces of video data for each generated small set.

[0074] When the multiple pieces of video data belonging to the first group include image data and video data, the generation unit 160 may give priority to selecting the video data.

[0075] Furthermore, the generating section 160 may preferentially select video data of the most numerous event type among the event types of the multiple video data belonging to the first group.

[0076] Alternatively, when the generation unit 160 selects a predetermined number of video data from the plurality of video data belonging to the first group, half of the predetermined number may be selected in order from the video data of the event types having the greatest number among the event types of the plurality of video data belonging to the first group.

[0077] Furthermore, for example, the generating unit 160 selects, as the remaining half of the predetermined number, video data that have not been selected as half of the predetermined number, from the event types of the plurality of video data belonging to the first group.

[0078] Alternatively, the generating section 160 may select the remaining half of the predetermined number in order from the video data of the event types having the least number of event types among the plurality of video data belonging to the first group.

[0079] The decoder unit 171 is a processing unit that decodes (eg, decompresses) encoded (eg, compressed) content so that the content, such as video data, can be displayed on the display device 300 when the content is output from the output unit 170.

[0080] Processing units such as the acquisition unit 120, the control unit 130, the estimation unit 140, the classification unit 150, the generation unit 160, and the decoder unit 171 are realized, for example, by a control program stored in the memory unit 190 and a processor 130a that executes the control program.

[0081] The various processing units may be realized by different processors, or may be realized by the same processor.

[0082] The output unit 170 is a communication interface that outputs a plurality of pieces of video data based on the slide show data generated by the generation unit 160. The output unit 170 is realized, for example, by a content output IF 170a. The output unit 170 may communicate with the display device 300 by wired or wireless communication. The communication unit 110 and the output unit 170 may be realized by a single piece of hardware (more specifically, a communication interface).

[0083] Furthermore, the output unit 170 may output the slide show data and the multiple pieces of video data to the display device 300. In this case, for example, the display device 300 sequentially displays the videos indicated by the multiple pieces of video data based on the slide show data. Alternatively, the output unit 170 may sequentially output the video data based on the slide show data. In this case, the display device 300 sequentially displays the videos indicated by the output video data.

[0084] The receiving unit 180 is a device that receives an instruction from a user. The receiving unit 180 receives, for example, an optical signal emitted by an operation console operated by a user. The receiving unit 180 is realized, for example, by a light receiving sensor 180a.

[0085] The storage unit 190 is a storage device that stores a plurality of pieces of video data, and is realized by, for example, a recording medium 190a.

[0086] [1-2. Operation] The operation of the slide show generation device 100 configured as above will now be described.

[0087] <Processing Procedure> FIG. 4 is a flowchart showing a procedure for generating slide show data by the slide show generating device 100 according to the embodiment.

[0088] First, the slide show generation device 100 captures video data from the imaging device 200 (step S101). For example, the control unit 130 receives the video data via the communication unit 110.

[0089] Next, slideshow generating device 100 writes, that is, stores, the imported video data in storage unit 190 (step S102). For example, control unit 130 stores the video data received via communication unit 110 in storage unit 190 via acquisition unit 120. For example, control unit 130 creates folders for each date and stores the data in storage unit 190, and when control unit 130 receives video data via communication unit 110, it classifies the video data into each folder based on the date the video data was generated.

[0090] The slide show generation device 100 stores a plurality of pieces of video data in the storage unit 190, for example, by repeating steps S101 and S102.

[0091] Next, the acquisition unit 120 acquires a plurality of pieces of video data stored in the storage unit 190 (step S103).

[0092] Next, the estimation unit 140 performs image analysis on each of the plurality of video data acquired by the acquisition unit 120, thereby estimating the type of event for each of the plurality of video data (step S104).

[0093] Next, the classification unit 150 classifies the image data into one of a plurality of groups based on a predetermined condition (step S105). Note that the classification of the image data will be described in detail later.

[0094] Next, the classification unit 150 classifies the video data into one of a plurality of groups based on a predetermined condition (step S106). For example, the classification unit 150 classifies the video data into one of the groups to which the image data classified into the plurality of groups in step S105 belongs. Details of the classification of the video data will be described later.

[0095] Next, the generating unit 160 selects, for each group classified by the classifying unit 150, one or more pieces of video data from the plurality of pieces of video data belonging to the group (step S107).

[0096] Next, the generation unit 160 generates slide show data for sequentially playing back the one or more pieces of video data selected for each group classified by the classification unit 150 (step S108). In this manner, the slide show generation device 100 generates slide show data for each group.

[0097] The generating unit 160 stores the generated slide show data in the storage unit 190.

[0098] FIG. 5 is a flowchart showing a procedure for playing back a slide show by the slide show generating device 100 according to the embodiment.

[0099] For example, it is assumed that the slide show generation device 100 generates slide show data and scenario list information by executing steps S101 to S108, and then the power is turned off by the user.

[0100] The slide show generation device 100 determines whether or not the power has been turned on (step S109). For example, the control unit 130 determines whether or not a signal indicating an instruction to turn on the power has been received from the user via the receiving unit 180, or whether or not a power button (not shown) has been pressed.

[0101] If the control unit 130 determines that the power of the slide show generation device 100 is not turned on (No in step S109), the control unit 130 returns the process to step S109.

[0102] On the other hand, when the control unit 130 determines that the power of the slideshow generating device 100 has been turned ON (Yes in step S109), it causes the acquisition unit 120 to acquire slideshow data from the storage unit 190 (step S110). Here, for example, the control unit 130 generates scenario list information that is a list of slideshow data for each group. The scenario list information is, for example, data that lists the title of each slideshow data. The title may be determined arbitrarily. The title may be the date or place when the video data belonging to the group was generated, or the type of event of the video data belonging to the group, etc.

[0103] Next, the control unit 130 causes the output unit 170 to output the scenario list information to the display device 300, thereby causing the display device 300 to display the scenario list information (step S111).

[0104] Next, control unit 130 acquires a scenario selection from the user (step S112). For example, the user operates the operation console to select one scenario from among multiple scenarios (e.g., titles of slideshow data) displayed on display device 300 as scenario list information, and causes a signal indicating the selected scenario to be transmitted to the operation console. Control unit 130 receives a signal indicating the scenario selected by the user, for example, via receiving unit 180. As a result, control unit 130 acquires the scenario selection.

[0105] Next, control unit 130 causes output unit 170 to sequentially output the video data based on the selected scenario, i.e., based on the slide show data of the selected scenario, thereby sequentially displaying images or videos indicated by the video data on display device 300 (step S113). Of course, control unit 130 may cause output unit 170 to collectively output to display device 300 a plurality of video data and slide show data indicating the order in which the plurality of video data are to be displayed.

[0106] 6 is a flowchart showing the processing procedure for classifying image data in the slide show generation device 100 according to the embodiment. Specifically, FIG. 6 is a flowchart showing the details of step S105.

[0107] The first classification unit 151 selects one of the folders in which image data is classified that are stored in the storage unit 190 (step S201). As described above, for example, the control unit 130 classifies the video data into folders created for each date based on the date on which the video data was generated. That is, each folder contains video data generated on the same date. For example, the first classification unit 151 selects a folder that stores image data generated earliest from among folders that store unclassified image data that has not been classified into groups from among multiple folders.

[0108] Next, the first classification unit 151 selects image data that has not yet been classified into any group, in other words, does not belong to any group, from among the image data stored in the selected folder (step S202). When there are multiple image data that do not belong to any group, the first classification unit 151 may select any one of them.

[0109] Next, the first classification unit 151 determines whether or not there is a group to which video data with the same creation date as that of the selected image data belongs (step S203).

[0110] If the first classification unit 151 determines that a group exists to which video data with the same generation date as the selected image data belongs (Yes in step S203), it determines whether the selected image data was generated in the same place as the image data belonging to the group determined to exist (step S204).

[0111] If the first classification unit 151 determines that the selected image data is generated in the same place as the image data belonging to the group determined to exist (Yes in step S204), it selects the group determined to exist (step S205).

[0112] On the other hand, if the first classification unit 151 determines No in step S203 or determines No in step S204, it generates and selects a new group (step S206).

[0113] After step S205 or step S206, the first classification unit 151 adds the selected image data to the selected group (step S207). In other words, the first classification unit 151 links the selected image data to the selected group so that the selected image data belongs to the selected group.

[0114] Next, the first classification unit 151 determines whether or not there is unclassified image data in the selected folder (step S208).

[0115] When first classification unit 151 determines that unclassified image data exists in the selected folder (Yes in step S208), it returns the process to step S202.

[0116] On the other hand, if first classification unit 151 determines that there is no unclassified image data in the selected folder (No in step S208), it determines whether the number of video data belonging to each group is equal to or greater than a predetermined number (step S209).

[0117] When first classification unit 151 determines that the number of video data belonging to each group is equal to or greater than a predetermined number (Yes in step S209), it ends the classification process of the image data.

[0118] On the other hand, if the first classification unit 151 determines that the number of video data belonging to each group is not greater than or equal to the predetermined number (No in step S209), that is, if it determines that there is a group to which the number of video data belonging thereto is less than the predetermined number, it proceeds to addition processing.

[0119] FIG. 7 is a flowchart showing the processing procedure of the additional processing of classifying image data (that is, the continuation of the processing if No in step S209) of the slide show generation device 100 in this embodiment.

[0120] The third classification unit 153 determines whether or not the location where the video data (more specifically, image data) belonging to a group having less than a predetermined number of video data belonging to the group was generated is a predetermined distance or more away from the predetermined location (step S210). For example, it is assumed that the predetermined location is a home and the predetermined distance is 30 km. In this case, the third classification unit 153 determines whether or not the location where the image data was generated is 30 km or more away from the home.

[0121] If the third classification unit 153 determines that the location where the video data belonging to a group with less than a predetermined number of video data was generated is not away from the predetermined location by more than a predetermined distance (No in step S210), it terminates the addition process (more specifically, the classification process of the image data).

[0122] On the other hand, when the third classification unit 153 determines that the position where the video data belonging to a group with less than the predetermined number of video data was generated is a predetermined distance or more away from the predetermined position (Yes in step S210), it selects the folder the day after the folder selected in step S201 (step S211). In other words, when the result in step S210 is Yes, the third classification unit 153 selects a folder storing image data generated the day after the date and time when the image data stored in the folder selected in step S201 was generated.

[0123] Next, the third classification unit 153 selects image data that has not been classified into any group from among the image data stored in the selected folder (step S212).

[0124] Next, the third classification unit 153 determines whether or not the position where the selected image data was generated coincides with the position where the video data belonging to a group having less than a predetermined number of video data was generated (step S213).

[0125] If the third classification unit 153 determines that the location where the selected image data was generated matches the location where the video data belonging to a group with less than a predetermined number of video data was generated (Yes in step S213), it adds the selected image data to the group (step S214).

[0126] After step S214, or if the result of step S213 is No, the third classification unit 153 determines whether or not there is image data in the selected folder that has not been classified into any group (i.e., unclassified) (step S215).

[0127] When third classification unit 153 determines that the selected folder contains image data that has not been classified into any group (Yes in step S215), it returns the process to step S212.

[0128] On the other hand, if the third classification unit 153 determines that there is no image data in the selected folder that has not been classified into any group (No in step S215), it determines whether any of the image data stored in the selected folder has been added to less than a predetermined number of groups (step S216).

[0129] If the third classification unit 153 determines that none of the image data stored in the selected folder has been added to a group with less than a predetermined number of groups (Yes in step S216), it terminates the addition process (more specifically, the classification process of the image data).

[0130] On the other hand, if third classification unit 153 determines that even one image data stored in the selected folder has been added to less than the predetermined number of groups (No in step S216), it returns the process to step S209.

[0131] For example, if the answer is No in step S216, and further if the answer is No in step S209 and further if the answer is Yes in step S210, the third classification unit 153 further selects the folder for the next day in step S211. In this way, if there is a group having less than a predetermined number of video data belonging to the group, the third classification unit 153 adds image data generated on or after the day after the image data belonging to the group to the group according to the generation position.

[0132] 8 is a flowchart showing the procedure of the video data classification process of the slideshow generation device 100 according to the embodiment. Specifically, FIG 8 is a flowchart showing the details of step S106.

[0133] The second classification unit 152 selects one of the folders in which the video data stored in the storage unit 190 has been classified (step S301). For example, the second classification unit 152 selects a folder that stores the earliest generated video data from among folders in which unclassified video data that has not been classified into groups is stored from among a plurality of folders.

[0134] Next, the second classification unit 152 acquires classification information (Step S302).

[0135] The classification information is information on groups into which the video data is classified. As described above, the image data is classified into one of the groups by executing steps S201 to S216. The second classification unit 152 classifies the video data into one of the groups included in the classification information based on the classification information, for example, through the following processing.

[0136] Next, the second classification unit 152 selects video data that is not classified into any group from the video data stored in the selected folder (Step S303).

[0137] Next, the second classification unit 152 determines whether there are multiple groups to which video data with the same generation date as the date (generation date) on which the selected video data is generated (step S304). That is, the second classification unit 152 determines whether there are multiple groups to which video data with the same generation date as the selected video data belongs, or whether there is only one group.

[0138] If the second classification unit 152 determines that there are multiple groups to which video data with the same generation date as the selected video data belongs (Yes in step S304), it selects from the multiple groups the group to which the video data closest to the generation time of the selected video data belongs (step S305).

[0139] On the other hand, if the second classification unit 152 determines that there is only one group to which video data with the same generation date as the selected video data belongs (No in step S304), it selects the only group (step S306).

[0140] Note that, for example, if the second classification unit 152 determines in step S304 that there is no image data with the same generation date as the selected video data, it may generate and select a new group.

[0141] After step S305 or step S306, the second classification unit 152 adds the selected video data to the selected group (step S307).

[0142] Next, the second classification unit 152 determines whether or not the selected folder contains video data that has not been classified into any group, that is, unclassified video data (Step S308).

[0143] If the second classification unit 152 determines that the selected folder contains video data that has not been classified into any group (Yes in step S308), the process returns to step S304.

[0144] On the other hand, if second classification unit 152 determines that the selected folder does not contain any video data that has not been classified into any group (No in step S308), it ends the classification process of the video data.

[0145] Fig. 9 is a flowchart showing the procedure for generating slide show data by the slide show generating device 100 in the embodiment. Specifically, Fig. 9 is a flowchart showing the details of steps S107 and S108. Note that Fig. 9 describes the procedure for generating slide show data for one of the groups classified by the classification unit 150. The generation unit 160 executes the following process for each group to generate slide show data for each group. In the explanation of Fig. 9, it is assumed that a plurality of video data belong to a group for which the generation unit 160 generates slide show data.

[0146] The generating unit 160 arranges the multiple video data belonging to a group in chronological order, starting from the video data with the earliest generation date and time, that is, in chronological order, and classifies the arranged multiple video data into multiple small sets (step S401). For example, the generating unit 160 divides the total period from the date and time when the first video data in the arranged multiple video data was generated to the date and time when the last video data was generated into N equal parts, and generates multiple small sets by classifying the arranged multiple video data into each of the N equal parts of the total period. Alternatively, for example, the generating unit 160 generates multiple small sets by classifying the arranged multiple video data into M parts in the arranged order.

[0147] Next, the generating unit 160 selects, from among the multiple small sets, the small set that is at the top in time series (step S402).

[0148] Next, the generator 160 determines whether or not there is any unselected video data among the video data belonging to the selected small set (Step S403).

[0149] When the generating unit 160 determines that there is unselected video data among the video data belonging to the selected small set (Yes in step S403), it selects the unselected video (step S404). For example, when there are multiple unselected videos, the generating unit 160 selects one of them. In this case, the generating unit 160 may select the video randomly, or, if the selected group is a group classified by event type, may preferentially select videos that are highly related to the event type based on the result of image analysis.

[0150] On the other hand, if the generation unit 160 determines that there is no unselected video data among the video data belonging to the selected small set (No in step S403), it selects image data of an event type that has the highest degree of match with a specified event type among the image data belonging to the selected small set (step S405).

[0151] The type of the predetermined event may be set arbitrarily and is not particularly limited. The type of the predetermined event may be determined in advance by a user, or, for example, the generating unit 160 may determine the most common type of event among the types of events of a plurality of video data belonging to a group as the type of the predetermined event.

[0152] After step S404 or step S405, the generation unit 160 determines whether the number of selected video data has reached a predetermined number (step S406).

[0153] If the generation unit 160 determines that the number of selected video data has not reached a predetermined number (No in step S406), it determines whether the selected subset is the last subset in chronological order (step S407).

[0154] When the generating unit 160 determines that the selected small set is the last small set in the time series (Yes in step S407), the processing returns to step S402.

[0155] On the other hand, if the generation unit 160 determines that the selected small set is not the last small set when viewed in chronological order (No in step S407), it selects the small set that is next to the selected small set when viewed in chronological order (step S408), and returns the process to step S403.

[0156] The generating unit 160 repeats the processes of steps S402 to S408 until the selected video data reaches a predetermined number, thereby selecting video data from a plurality of small sets without bias in number.

[0157] If the generation unit 160 determines that the number of selected video data has reached a predetermined number (Yes in step S406), it generates slide show data for sequentially playing the selected video data (step S409), and terminates the process of generating the slide show data.

[0158] Note that, if the number of video data belonging to a group is less than a predetermined number, generation unit 160 may select all of the video data belonging to the group and execute step S409, or may not generate slide show data for the group.

[0159] <Example> Each process executed by the slide show generating device 100 will be described in detail below with reference to Figures 10 to 17. Note that, hereinafter, image data may be simply referred to as photos, and moving image data may be simply referred to as moving images.

[0160] FIG. 10 is a diagram showing a first example of classification of image data in the slide show generation device 100 according to the embodiment.

[0161] The first classification unit 151 classifies, for example, photos A, B, C, and D that were generated at a predetermined distance or more from home and while "away from home" into the same group (for example, the first group).

[0162] It should be noted that the photos A to D may have the same or different creation dates. Assume that the first classification unit 151 classifies the photos A to C into the first group by executing, for example, steps S201 to S208, and determines No in step S209. In this case, the first classification unit 151 and the third classification unit 153 may classify the photo D, which was created the day after the photos A to C, into the first group by repeatedly executing, for example, steps S209 to S216.

[0163] FIG. 11 is a diagram showing a second example of classification of image data in the slide show generation device 100 according to the embodiment.

[0164] For example, the first classification unit 151 classifies photos A to D created on the same day into different groups based on the location where they were created. For example, it is assumed that the GPS data for destination A indicates GPS1, and the GPS data for destination B indicates GPS2. It is also assumed that photo A includes location information indicating GPS1, photo B includes location information indicating GPS1, photo C includes location information indicating GPS2, and photo D includes location information indicating GPS2. In this case, the first classification unit 151 classifies, for example, photos A and B into different groups from photos C and D into different groups. For example, the first classification unit 151 classifies photos A and B into the same group (for example, the first group) and photos C and D into the same group (for example, the second group).

[0165] Fig. 12 is a diagram showing a first example of classification of moving image data by the slideshow generation device 100 in the embodiment. Note that the dates and times when each of the photos A to D was generated are shown at the top of the page of the photos A to D and the moving image A shown in Fig. 12. For example, the photo A was generated at 12 o'clock on July 3rd.

[0166] For example, suppose that photos A to D have been classified into a first group by the first classification unit 151. As shown in Fig. 12, for example, the creation dates of photos A to D belonging to the first group are all July 3, and the creation date of video A is July 3. In this case, the second classification unit 152 selects video A in step S303, and when determining No in step S304, selects the first group in step S306 and adds video A to the first group in step S307.

[0167] Fig. 13 is a diagram showing a second example of classification of moving image data by the slideshow generation device 100 in the embodiment. Note that the dates and times when each of the photographs A to D was generated are indicated above the photographs A to D and the moving image A shown in Fig. 13.

[0168] For example, suppose that the first classification unit 151 classifies photos A and B into a first group, and photos C and D into a second group. As shown in FIG. 13, for example, the generation dates of photos A to D are all July 3, and the generation date of video A is July 3. In this case, the second classification unit 152 determines Yes in step S304. Here, photo B belonging to the first group is generated latest among the video data belonging to the first group, and the generation time is 12:30. Furthermore, photo C belonging to the second group is generated earliest among the video data belonging to the second group, and the generation time is 14:00. Furthermore, the generation time of video A is 12:32. In other words, the generation time of video A is closer to that of photo B than that of photo C. Therefore, in this case, the second classification unit 152 selects the first group in step S305, and adds video A to the first group in step S307.

[0169] Fig. 14 is a diagram showing a third example of classification of moving image data by the slideshow generation device 100 according to the embodiment. Note that the event types of the photos A to D are indicated above the pages of the photos A to D and the moving image A shown in Fig. 14. For example, the event type of the photo A is an athletic meet.

[0170] For example, suppose that first classification unit 151 classifies photos A and B into a first group, and photos C and D into a second group. Here, suppose that the event type of all the video data belonging to the first group is an athletic meet, and the event type of all the video data belonging to the second group is an outing. Also suppose that the event type of video A is an athletic meet. In this case, second classification unit 152 may classify video A into the first group. In this way, second classification unit 152 may determine the group to which video data belongs based on the event type of the video data.

[0171] Fig. 15 is a diagram showing a fourth example of classification of moving image data by the slideshow generation device 100 in the embodiment. Note that the dates and times when each of the photographs A to L was generated are indicated above the photographs A to L shown in Fig. 15.

[0172] For example, in step S401, the generating unit 160 arranges the photos A to L in chronological order, and generates a plurality of small sets by classifying the arranged photos A to L into groups of M items (in this example, M=3) in the arranged order. The generating unit 160 classifies, for example, photos A to C into a first small set, photos D to F into a second small set, photos G to I into a third small set, and photos J to L into a fourth small set.

[0173] Fig. 16 is a diagram showing a fifth example of classification of moving image data by the slideshow generation device 100 in the embodiment. Note that the dates and times when each of the photographs A to L was generated are indicated above the photographs A to L shown in Fig. 16.

[0174] Unlike the example described with reference to Fig. 15, in step S401, the generating unit 160 may equally divide the total period from the date and time when the first image data (in this example, photo A) of photos A to L arranged in chronological order was generated to the date and time when the last image data (in this example, photo L) was generated into N (in this example, N = 4) and generate a plurality of small sets by classifying the arranged plurality of image data into each period of the N equally divided total period. For example, it is assumed that photo A was generated at 12:00 and photo L was generated at 16:00. In this case, the total period is four hours from 12:00 to 16:00. Therefore, when N=4, the generation unit 160 classifies the photos A to L into one of the following small sets: a first small set to which the video data generated between 12:00 and 13:00 belongs, a second small set to which the video data generated between 13:00 and 14:00 belongs, a third small set to which the video data generated between 14:00 and 15:00 belongs, and a fourth small set to which the video data generated between 15:00 and 16:00 belongs. In this example, the generation unit 160 classifies, for example, the photos A to C into the first small set, the photos D to F into the second small set, the photos G and H into the third small set, and the photos I to L into the fourth small set.

[0175] The above-mentioned M and N may be arbitrarily determined in advance.

[0176] Fig. 17 is a diagram showing a sixth example of classification of moving image data by the slideshow generation device 100 in the embodiment. Note that the dates and times when each of the photographs A to J and the videos A and B was generated are indicated above the photographs A to J and the videos A and B shown in Fig. 17.

[0177] For example, the generating unit 160 rearranges the photos A to J and the videos A and B belonging to the first group in chronological order as shown in FIG. 17 and classifies them into a plurality of small sets. In this case, the generating unit 160 selects, for example, one piece of video data from the plurality of video data belonging to the first small set, further selects one piece of video data from the plurality of video data belonging to the second small set, further selects one piece of video data from the plurality of video data belonging to the third small set, and selects one piece of video data from the plurality of video data belonging to the fourth small set. Also, for example, if the number of selected pieces of video data does not reach a predetermined number, the generating unit 160 further selects one piece of video data from the first small set. For example, the generating unit 160 preferentially selects video data from among the video data belonging to the small set. For example, when selecting one piece of video data from among the plurality of video data belonging to the second small set, the generating unit 160 selects video A. If there is no video data from the video data belonging to the small set, the generating unit 160 may select any image data. When selecting one piece of video data from among the plurality of pieces of video data belonging to the first small set, the generating unit 160 may select photo A, which has the earliest generation date, from among the plurality of pieces of video data belonging to the first small set. Alternatively, as described above, the generating unit 160 may select image data based on the type of event if there is no video data from among the video data belonging to the small set.

[0178] <Summary> The above-described slide show generation device 100 operates, for example, as follows.

[0179] FIG. 18 is a flowchart showing the processing procedure of the slide show generation device 100 according to the embodiment.

[0180] First, the acquiring unit 120 acquires a plurality of pieces of video data (step S501). For example, the acquiring unit 120 acquires a plurality of pieces of video data stored in the storage unit 190 from the storage unit 190 as in step S103.

[0181] Next, the first classification unit 151 classifies the first video data generated on the same day and at the same place into a first group among the multiple video data acquired by the acquisition unit 120 (step S502). For example, when the date and time and place of generation of the multiple video data are known (i.e., the video data includes date and time information indicating the date and time of generation and place information indicating the place of generation), the first classification unit 151 classifies the video data into multiple groups for each date and place of generation.

[0182] Next, the second classification unit 152 classifies, into the first group, the second video data, which is video data that does not include location information indicating a location where the data was generated, and which was generated between the dates and times when the first video data belonging to the first group were generated, among the multiple video data acquired by the acquisition unit 120 (step S503). For example, as described with reference to FIG. 12, the second classification unit 152 determines whether the second video data was generated between the dates and times when the first video data belonging to the first group were generated, based on date and time information included in the first video data belonging to the first group and date and time information included in the second video data. When the second classification unit 152 determines that the second video data was generated between the dates and times when the first video data belonging to the first group were generated, it classifies the second video data into the first group.

[0183] Next, the generator 160 selects one or more pieces of image data from among the plurality of image data that belong to the first group and that are the first image data or the second image data (Step S504).

[0184] Next, the generation unit 160 generates slide show data for sequentially playing back the one or more pieces of video data selected in step S504 (step S505).

[0185] [1-3. Effects, etc.] As described above, the slideshow generating method in the embodiment includes an acquisition step (step S501) of acquiring multiple pieces of video data, a first classification step (step S502) of classifying into a first group first video data generated on the same day and at the same place from among the multiple pieces of video data acquired in the acquisition step, a second classification step (step S503) of classifying into a first group second video data, which is video data from the multiple pieces of video data acquired in the acquisition step that does not include location information indicating the place of generation, and which was generated between the dates and times when each of the multiple pieces of first video data belonging to the first group was generated, and a generation step (step S505) of selecting one or more pieces of video data from among the multiple pieces of video data belonging to the first group, each of which is first video data or second video data (step S504), and generating slideshow data for sequentially playing back the selected one or more pieces of video data.

[0186] According to this, for example, image data (an example of first video data) including information on the date and time of generation and the location can be classified based on the date and time of generation and the location. Furthermore, moving image data (an example of second video data) including information indicating the date and time of generation but not information indicating the location of generation can be classified into the same group as image data generated close in time. Video data generated close in time is highly likely to have been generated at the same location. Therefore, according to this classification method, it is possible to easily group together video data generated at the same location, that is, video data of the same event occurring during the same period. In other words, according to the slideshow generation method of the present disclosure, appropriate video data to be played can be selected.

[0187] Furthermore, for example, the video data includes identification data indicating an imaging device that generated the video data. In this case, for example, in the second classification step, when the date and time when the second video data was generated is between the dates and times when the multiple first video data belonging to the first group were generated, and when the imaging device indicated by the identification data included in the second video data matches the imaging device indicated by the identification data included in the multiple first video data belonging to the first group, the second video data is classified into the first group.

[0188] If the video data were generated by the same imaging device, it is highly likely that the video data were generated in the same location. In other words, if the video data were generated by the same imaging device, it is highly likely that the video data were of the same event that occurred during the same period. Therefore, this makes it possible to select more appropriate video data to be played.

[0189] Also, for example, in the first classification step, for each of the multiple first image data among the multiple image data acquired in the acquisition step, a distance between the location where the image data was generated and a predetermined first location is calculated. In this case, for example, in the first classification step, the first image data is classified into different groups into close-distance first image data, which is first image data whose calculated distance is less than the predetermined first distance, and long-distance first image data, which is first image data whose calculated distance is equal to or greater than the predetermined first distance.

[0190] Video data generated at a close distance is more likely to be video data of the same event occurring in the same time period than video data generated at a long distance, making it possible to select more appropriate video data to be played back.

[0191] Also, for example, in the first classification step, among the plurality of image data acquired in the acquisition step, the plurality of first image data belonging to the first group are classified into two groups, one of which is generated on a different date and one of which is generated at a different place. TheThe third image data is classified into a second group. In addition, for example, in the second classification step, when viewed in chronological order, the date and time when the second image data was generated is between the date and time when each of the plurality of first image data belonging to the first group was generated and the date and time when each of the plurality of third image data belonging to the second group was generated, the second image data is classified into a group to which the image data generated at the date and time closest to the date and time when the second image data was generated belongs, among the image data classified into either the first group or the second group.

[0192] According to this, after classifying the first image data, even if the second image data was not generated between the dates and times when the multiple first image data in each group were generated, the second image data can be classified into a group including first image data generated at a close date and time.

[0193] Alternatively, for example, the slideshow generating method of the present disclosure further includes an estimation step (for example, step S104) of estimating the type of event of the video data by image analysis of the video data. In this case, for example, in the first classification step, among the multiple video data acquired in the acquisition step, the multiple first video data belonging to the first group are classified into two types, namely, the type of event of the video data acquired in the acquisition step, which is different from the multiple first video data belonging to the first group in at least one of the date and place of generation. The In this case, for example, in the second classification step, when viewed in chronological order, the date and time when the second video data was generated is between the date and time when each of the plurality of first video data belonging to the first group was generated and the date and time when each of the plurality of third video data belonging to the second group was generated, the second video data is classified into a group to which video data of an event type having the highest matching rate with the event type of the second video data belongs, among the video data classified into either the first group or the second group.

[0194] According to this, after classifying the first video data, even if the second video data was not generated between the dates and times when the multiple first video data of each group were generated, the second video data can be classified into a group that contains a large amount of first video data of the same event that occurred during the same period.

[0195] Also, for example, the slideshow generating method of the present disclosure further includes a third classification step (for example, steps S209 to S216) of determining whether or not the number of the plurality of video data belonging to the first group is less than a predetermined number. In this case, for example, in the third classification step, if it is determined that the number of the plurality of video data belonging to the first group is less than a predetermined number (No in step S209), it is determined whether or not the location where the first video data belonging to the first group was generated is a location that is a predetermined second distance or more away from the predetermined second location (for example, step S210). Also, for example, in the third classification step, if it is determined that the location where the first video data belonging to the first group was generated is a location that is a predetermined second distance or more away from the predetermined second location (Yes in step S210), it is determined whether or not the plurality of video data belonging to the first group was generated on or after the day after the date on which each of the plurality of video data belonging to the first group was generated, among the plurality of video data acquired in the acquisition step. Film Extract the image data (e.g., steps S211 and S212) Film In the third classification step, it is determined whether or not the location where the image data was generated is the same as the location where the first image data belonging to the first group was generated (for example, step S213). Film If it is determined that the location where the image data was generated is the same as the location where the first image data belonging to the first group was generated (for example, Yes in step S213), Film The image data is classified into a first group (eg, step S214).

[0196] If a slide show for a group is generated when the number of pieces of video data belonging to the group is extremely small, the slide show will display the same video data frequently. Therefore, in a group with an extremely small number of pieces of video data, video data generated on or after the day after the video data belonging to the group is included. For example, if the video data is generated at a location some distance from the user's home, the video data can be classified as a group that includes a large number of video data of a rough event type, such as a user going out for several days.

[0197] Also, for example, in the generating step, the multiple video data included in the first group are arranged in chronological order starting from the video data with the earliest generation date and time, the total period from the date and time the first video data in the arranged multiple video data was generated to the date and time the last video data was generated is divided into N equal parts (N is an integer of 2 or more), the arranged multiple video data are classified into each of the N equal parts of the total period to generate multiple small sets (for example, step S401 and FIG. 16), and one or more video data are selected for each generated small set (for example, steps S402 to S408 and FIG. 17).

[0198] If a slide show including all the video data of a certain group is generated when there is an extremely large amount of video data belonging to that group, the slide show may be long or the display time of each video data may be extremely short. Therefore, by extracting video data for each generated period and generating a slide show, the slide show can be generated from multiple video data that are distributed over time, without being generated only from multiple video data generated during a specific period. With such a slide show, for example, a user can easily look back on the entire event when viewing the slide show.

[0199] Also, for example, in the generating step, the multiple video data included in the first group are arranged in chronological order starting from the video data generated earliest, the arranged multiple video data are classified in the arranged order into multiple small sets of M pieces (M is an integer equal to or greater than 2) (for example, step S401 and FIG. 15), and one or more video data are selected for each of the generated small sets (for example, steps S402 to S408 and FIG. 17).

[0200] This also allows a slide show to be generated from multiple video data that are distributed over time, rather than being generated only from multiple video data generated during a specific period of time, which, for example, allows a user to easily review the entire event when viewing the slide show.

[0201] Also, for example, in the generating step, if the multiple video data belonging to the first group include image data and video data (for example, No in step S403), the video data is preferentially selected (step S404).

[0202] Since video data is more likely to contain more information than image data, a slide show with a large amount of information can be generated by preferentially selecting video data.

[0203] Also, for example, the slideshow generating method of the present disclosure further includes an estimation step of estimating an event type of the video data by image analysis of the video data. In this case, for example, in the generating step, video data of the most frequent event type is preferentially selected from among the event types of the multiple video data belonging to the first group.

[0204] With this, even if a group contains video data whose event type is completely different from that of other video data, a slide show can be generated using video data of the same event type without selecting that video data.

[0205] Also, for example, the slideshow generating method of the present disclosure further includes an estimation step of estimating the event type of the video data by image analysis of the video data. For example, in the generating step, when selecting a predetermined number of video data from a plurality of video data belonging to a first group, half of the predetermined number are selected in descending order of the number of event types of the plurality of video data belonging to the first group, and the remaining half of the predetermined number are selected from the event types of the plurality of video data belonging to the first group that are not selected as the half of the predetermined number.

[0206] According to this, the video data may include video data for which the type of event cannot be identified by the estimation unit 140. Even in such a case, the generation unit 160 can select the remaining half of the predetermined number from the video data that does not include the majority of events among the video data belonging to the first group, including the video data for which the type of event has not been estimated.

[0207] Also, for example, in the generation step, when a predetermined number of video data are selected from the plurality of video data belonging to the first group, the remaining half of the predetermined number are selected in order from the video data of the event types with the fewest number among the event types of the plurality of video data belonging to the first group.

[0208] For example, some users may participate in multiple events in a day and take pictures at each of the multiple events. Depending on the number of video data, all of the video data generated in that day may belong to the same group. In this case, for example, if video data of a type of event with a large number of events is selected from the types of events of multiple video data belonging to a certain group, events with a small number of video data may not be included in the slide show. In this case, from the viewpoint of making it easy for the user to look back on the day by watching the slide show, it is difficult for the user to look back on events with a small number of video data. Therefore, half of the predetermined number are selected from the types of event with a large number of events from the types of events of multiple video data belonging to the first group, and the remaining half of the predetermined number are selected from the types of event with a small number of events from the types of events of multiple video data belonging to the first group. This makes it possible to generate a slide show that is easy for the user to look back on even events with a small number of video data.

[0209] Furthermore, the present disclosure may be realized as a program for causing a computer to execute the slideshow generating method of the present disclosure.

[0210] Furthermore, the general or specific aspects of the present disclosure may be realized in a system, an apparatus, a method, an integrated circuit, or a computer program, or in a computer-readable non-transitory recording medium such as an optical disk, a HDD, or a semiconductor memory in which the computer program is stored.

[0211] Furthermore, the slideshow generating device 100 in the embodiment includes an acquisition unit 120 that acquires multiple pieces of video data, a first classification unit 151 that classifies into a first group first video data generated on the same day and at the same place from among the multiple pieces of video data acquired by the acquisition unit 120, a second classification unit 152 that classifies into the first group second video data, which is video data from the multiple pieces of video data acquired by the acquisition unit 120 that does not include location information indicating the place of generation, and which was generated between the dates and times when each of the multiple pieces of first video data belonging to the first group was generated, and a generation unit 160 that selects one or more pieces of video data from the multiple pieces of video data included in the first group, each of which is first video data or second video data, and generates slideshow data for sequentially playing back the selected one or more pieces of video data.

[0212] This provides the same effects as the slide show generating method of the present disclosure described above.

[0213] (Other embodiments) As described above, the embodiments have been described as examples of the technology disclosed in this application. However, the technology in this disclosure is not limited to these, and can be applied to embodiments in which modifications, substitutions, additions, omissions, etc. are appropriately made. In addition, it is also possible to combine the components described in the above embodiments to create new embodiments.

[0214] Therefore, other embodiments will be exemplified below.

[0215] For example, the wireless communication method between the slideshow generating device and the imaging device is not particularly limited, and examples of the wireless communication method include predetermined wireless communication standards such as Bluetooth (registered trademark), wireless LAN (Local Area Network), Wi-Fi (registered trademark), and ZigBee (registered trademark).

[0216] Also, for example, in the above embodiment, the slide show generating device acquires the video data by communicating with the imaging device, but this is not limited thereto. For example, the slide show generating device may acquire the video data from a device that does not have a function of generating video data, such as a server device or a USB (Universal Serial Bus) memory.

[0217] Also, for example, in the above embodiment, all or some of the components of the processing units such as the control unit, classification unit, and generation unit included in the slideshow generation device of the present disclosure may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU (Central Processing Unit) or a processor reading and executing a software program recorded on a recording medium such as an HDD (Hard Disk Drive) or a semiconductor memory.

[0218] In addition, components of the processing units such as the control unit, classification unit, and generation unit included in the slideshow generation device of the present disclosure may be configured with one or more electronic circuits. Each of the one or more electronic circuits may be a general-purpose circuit or a dedicated circuit.

[0219] The one or more electronic circuits may include, for example, a semiconductor device, an integrated circuit (IC), or a large scale integration (LSI). The IC or LSI may be integrated into one chip or into multiple chips. Here, the IC or LSI is referred to as an IC or LSI, but the name may vary depending on the degree of integration, and may be called a system LSI, a very large scale integration (VLSI), or an ultra large scale integration (ULSI). Also, a field programmable gate array (FPGA) that is programmed after the LSI is manufactured can be used for the same purpose.

[0220] In addition, the general or specific aspects of the present disclosure may be realized as a system, an apparatus, a method, an integrated circuit, or a computer program. Alternatively, the present disclosure may be realized as a computer-readable non-transitory recording medium such as an optical disk, a HDD, or a semiconductor memory in which the computer program is stored. For example, the present disclosure may be realized as a program for causing a computer to execute the slideshow generating method in the above-described embodiment. In addition, the program may be recorded on a computer-readable non-transitory recording medium such as a CD-ROM, or may be distributed via a communication channel such as the Internet.

[0221] As described above, the embodiments have been described as examples of the technology in the present disclosure. For this purpose, the attached drawings and detailed description have been provided.

[0222] Therefore, among the components described in the attached drawings and detailed description, not only are there components essential for solving the problem, but there may also be components that are not essential for solving the problem in order to illustrate the above technology. Therefore, the fact that such non-essential components are described in the attached drawings or detailed description should not be interpreted as immediately indicating that such non-essential components are essential.

[0223] Furthermore, since the above-described embodiments are intended to illustrate the technology in the present disclosure, various modifications, substitutions, additions, omissions, and the like can be made within the scope of the claims or their equivalents. [Industrial Applicability]

[0224] The present disclosure is applicable to recorders and the like that acquire video data from multiple devices and generate slideshow data. [Explanation of symbols]

[0225] 100 Slideshow Generator 110 Communications Department 110a Communication IF 120 Acquisition Department 130 Control section 130a Processor 140 Estimation part 150 Classification Department 151 1st Classification Division 152 Second Classification Division 153 Third Classification Division 160 Generation part 170 Output section 170a Content output IF 171 Decoder section 180 Receiving unit 180a Light receiving sensor 190 Storage section 190a Recording media 200 Imaging device 201, 202 Smartphone 203 Digital Camera 204 Video Camera 300 display device 300a TV 400 Slideshow Generation System

Claims

1. An acquisition step of acquiring a plurality of image data; a first classification step of classifying, into a first group, first image data generated at a same place on a same day, from among the plurality of image data acquired in the acquisition step; a second classification step of classifying into the first group second image data, which is image data that does not include location information indicating a location where the second image data was generated among the plurality of image data acquired in the acquisition step and was generated during the dates and times when each of the plurality of first image data belonging to the first group was generated; a generating step of selecting one or more pieces of video data from among a plurality of pieces of video data belonging to the first group, each of which is the first video data or the second video data, and generating slide show data for sequentially playing back the one or more pieces of selected video data, In the first classification step, Calculating a distance between a location where the first image data was generated and a predetermined first location for each of the first image data among the plurality of image data acquired in the acquiring step; classifying the first image data, which is the first image data whose calculated distance is less than a predetermined first distance, into different groups from the first image data, which is the first image data whose calculated distance is equal to or greater than the predetermined first distance, into different groups; Among the plurality of image data acquired in the acquiring step, a third image data which is different from the plurality of first image data belonging to the first group in at least one of a date and a place of generation is classified into a second group; In the second classification step, when a date and time when the second video data is generated is between a date and time when each of the plurality of first video data belonging to the first group is generated and a date and time when each of the plurality of third video data belonging to the second group is generated, the second video data is classified into a group to which the video data generated at a date and time closest to the date and time when the second video data is generated belongs, among the video data classified into either the first group or the second group. How to generate a slideshow.

2. An acquisition step of acquiring a plurality of video data; an estimation step of estimating a type of an event of the video data by performing image analysis on the video data; a first classification step of classifying, into a first group, first image data generated at a same place on a same day, from among the plurality of image data acquired in the acquisition step; a second classification step of classifying into the first group second image data, which is image data that does not include location information indicating a location where the second image data was generated among the plurality of image data acquired in the acquisition step and was generated during the dates and times when each of the plurality of first image data belonging to the first group was generated; a generating step of selecting one or more pieces of video data from among a plurality of pieces of video data belonging to the first group, each of which is the first video data or the second video data, and generating slide show data for sequentially playing back the one or more pieces of selected video data, In the first classification step, Calculating a distance between a location where the first image data was generated and a predetermined first location for each of the first image data among the plurality of image data acquired in the acquiring step; classifying the first image data, which is the first image data whose calculated distance is less than a predetermined first distance, into different groups from the first image data, which is the first image data whose calculated distance is equal to or greater than the predetermined first distance, into different groups; Among the plurality of image data acquired in the acquiring step, a third image data which is different from the plurality of first image data belonging to the first group in at least one of a date and a place of generation is classified into a second group; In the second classification step, when a date and time when the second video data is generated is between a date and time when each of the plurality of first video data belonging to the first group is generated and a date and time when each of the plurality of third video data belonging to the second group is generated, the second video data is classified into a group to which video data of an event type having a highest matching rate with an event type of the second video data belongs, among the video data classified into either the first group or the second group. How to generate a slideshow.

3. the video data includes identification data indicative of an imaging device that generated the video data; In the second classification step, when a date and time when the second video data was generated is between dates and times when each of the plurality of first video data belonging to the first group was generated, and when an imaging device indicated by identification data included in the second video data matches an imaging device indicated by identification data included in the plurality of first video data belonging to the first group, the second video data is classified into the first group. A slide show generating method according to claim 1 or 2.

4. Further, a third classification step of determining whether or not the number of the plurality of video data belonging to the first group is less than a predetermined number, In the third classification step, when it is determined that the number of the plurality of image data belonging to the first group is less than a predetermined number, it is determined whether or not a location where the first image data belonging to the first group was generated is a location that is a predetermined second distance or more away from a predetermined second location; when it is determined that the location where the first video data belonging to the first group was generated is a location that is a predetermined second distance or more away from a predetermined second location, extracting, from the plurality of video data acquired in the acquisition step, video data that was generated on or after the day after the date on which each of the plurality of video data belonging to the first group was generated, and determining whether or not the location where the extracted video data was generated is the same as the location where the first video data belonging to the first group was generated; When it is determined that the location where the extracted video data was generated is the same as the location where the first video data belonging to the first group was generated, the extracted video data is classified into the first group. A slide show generating method according to any one of claims 1 to 3.

5. In the generating step, The plurality of video data included in the first group are arranged in chronological order starting from the video data generated earliest; Dividing a total period from the date and time when the first video data in the arranged plurality of video data was generated to the date and time when the last video data was generated into N equal parts (N is an integer of 2 or more); generating a plurality of small sets by classifying the plurality of arranged video data into each of the N equal periods of the total period; Select one or more pieces of video data for each of the generated small sets. A slide show generating method according to any one of claims 1 to 4.

6. In the generating step, The plurality of video data included in the first group are arranged in chronological order starting from the video data generated earliest; generating a plurality of small sets by classifying the plurality of arranged video data into groups of M pieces (M is an integer of 2 or more) in the order of arrangement; Select one or more pieces of video data for each of the generated small sets. A slide show generating method according to any one of claims 1 to 5.

7. In the generating step, when the plurality of video data belonging to the first group include image data and moving image data, the moving image data is preferentially selected. A slide show generating method according to any one of claims 1 to 6.

8. Further, the method includes an estimation step of estimating a type of an event of the video data by performing image analysis on the video data, In the generating step, the video data of the most numerous event types is preferentially selected from among the event types of the plurality of video data belonging to the first group. A slide show generating method according to any one of claims 1 to 7.

9. Further, the method includes an estimation step of estimating a type of an event of the video data by performing image analysis on the video data, In the generating step, when a predetermined number of image data are selected from the plurality of image data belonging to the first group, selecting half of the predetermined number of video data items in the first group in descending order of the number of event types among the event types of the plurality of video data items belonging to the first group; The remaining half of the predetermined number is selected from among the event types of the plurality of video data belonging to the first group, the video data not selected as the half of the predetermined number. A slide show generating method according to any one of claims 1 to 8.

10. In the generating step, the remaining half of the predetermined number is selected from among the event types of the plurality of video data belonging to the first group, in order from the video data of the least numerous event types. The slide show generating method of claim 9.

11. A method for causing a computer to execute the slide show generating method according to any one of claims 1 to 10. program.

12. An acquisition unit that acquires a plurality of video data; a first classification unit that classifies first image data generated on the same day and at the same place from among the plurality of image data acquired by the acquisition unit into a first group; a second classification unit that classifies into the first group second video data that is video data that does not include location information indicating a location where the data was generated among the plurality of video data acquired by the acquisition unit, and that was generated during a period during which each of the plurality of first video data belonging to the first group was generated; a generating unit that selects one or more pieces of video data from among a plurality of pieces of video data included in the first group, each of which is a first piece of video data or a second piece of video data, and generates slide show data for sequentially playing back the one or more pieces of selected video data, The first classification unit is Calculating a distance between a location where the first image data was generated and a predetermined first location for each of the first image data among the plurality of image data acquired by the acquisition unit; classifying the first image data, which is the first image data whose calculated distance is less than a predetermined first distance, into different groups from the first image data, which is the first image data whose calculated distance is equal to or greater than the predetermined first distance, into different groups; classifying, into a second group, third image data that is different from the first image data in at least one of a date and a place of generation from the first image data that belong to the first group among the plurality of image data acquired by the acquisition unit; When a date and time when the second video data was generated is between a date and time when each of the plurality of first video data belonging to the first group was generated and a date and time when each of the plurality of third video data belonging to the second group was generated, the second classification unit classifies the second video data into a group to which the video data generated at a date and time closest to the date and time when the second video data was generated belongs, among the video data classified into either the first group or the second group. Slideshow generator.

13. An acquisition unit that acquires a plurality of video data; an estimation unit that estimates a type of an event in the video data by performing image analysis on the video data; a first classification unit that classifies first image data generated on the same day and at the same place from among the plurality of image data acquired by the acquisition unit into a first group; a second classification unit that classifies into the first group second video data that is video data that does not include location information indicating a location where the data was generated among the plurality of video data acquired by the acquisition unit, and that was generated during a period during which each of the plurality of first video data belonging to the first group was generated; a generating unit that selects one or more pieces of video data from among a plurality of pieces of video data included in the first group, each of which is a first piece of video data or a second piece of video data, and generates slide show data for sequentially playing back the one or more pieces of selected video data, The first classification unit is Calculating a distance between a location where the first image data was generated and a predetermined first location for each of the first image data among the plurality of image data acquired by the acquisition unit; classifying the first image data, which is the first image data whose calculated distance is less than a predetermined first distance, into different groups from the first image data, which is the first image data whose calculated distance is equal to or greater than the predetermined first distance, into different groups; classifying, into a second group, third image data that is different from the first image data in at least one of a date and a place of generation from the first image data that belong to the first group among the plurality of image data acquired by the acquisition unit; When a date and time when the second video data was generated is between a date and time when each of the plurality of first video data belonging to the first group was generated and a date and time when each of the plurality of third video data belonging to the second group was generated, the second classification unit classifies the second video data into a group to which video data of an event type having a highest matching rate with an event type of the second video data belongs, among the video data classified into either the first group or the second group. Slideshow generator.

Images