Information processing device, information processing method, and program

Abstract

To provide an information processing device and the like capable of generating an appropriate layout.SOLUTION: An information processing device 1 includes: an acquisition unit configured to acquire a plurality of contents; a generation unit configured to generate a layout image in which the plurality of contents are arranged within a prescribed area; an evaluation unit configured to acquire an evaluation on the generated layout image by using a discriminator having learned a plurality of layout images; and an output unit configured to output an evaluation result. Thus, an appropriate layout can be generated.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Various methods have been proposed to support the creation of layouts for magazines, books, newspapers, etc. For example, Patent Document 1 discloses an information processing device that calculates the degree of association between content such as text and images, which are layout materials, and determines a layout so that content with a high degree of association is placed close to each other. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-169536 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the invention of Patent Document 1 merely arranges highly related contents close to each other, and there is a risk that the layout may not be appropriate when viewed as a whole.

[0005] In one aspect, an object is to provide an information processing device or the like that can generate an appropriate layout. [Means for solving the problem]

[0006] In one aspect, the information processing device is characterized by comprising an acquisition unit that acquires multiple contents, a generation unit that generates a layout image in which the multiple contents are arranged within a predetermined area, an evaluation unit that acquires an evaluation of the generated layout image using a classifier that has been trained on the multiple layout images, and an output unit that outputs the evaluation result. [Effects of the Invention]

[0007] In one aspect, an appropriate layout can be generated. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of the configuration of a layout generation system. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of a server. [Figure 3] FIG. 1 is an explanatory diagram showing an overview of the present embodiment. [Figure 4] FIG. 10 is an explanatory diagram relating to layout learning processing. [Figure 5] FIG. 10 is an explanatory diagram relating to a layout generation process. [Figure 6] 10 is a flowchart illustrating an example of a processing procedure for layout learning processing. [Figure 7] 10 is a flowchart illustrating an example of a processing procedure for a layout generation process. [Figure 8] FIG. 10 is an explanatory diagram showing an overview of a second embodiment. [Figure 9] 10 is a flowchart showing an example of a processing procedure for a layout generation process according to the second embodiment. [Figure 10] FIG. 2 is a functional block diagram showing the operation of the server of the above-described embodiment. [Figure 11] FIG. 2 is an explanatory diagram showing the main parts of a scoring model 141. [Figure 12] FIG. 10 is an explanatory diagram showing an example of calculation of a total score. [Figure 13] FIG. 10 is an explanatory diagram showing an overview of a fourth embodiment. [Figure 14] 13 is a flowchart showing an example of a processing procedure for a layout generation process according to the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The present invention will be described in detail below with reference to the drawings showing embodiments thereof. (Embodiment 1) Fig. 1 is a schematic diagram showing an example of the configuration of a layout generation system. In this embodiment, a layout generation system that automatically generates page layouts for documents such as magazines, books, newspapers, etc. will be described. The layout generation system includes an information processing device 1 and a terminal 2. Each device is communicatively connected via a network N such as the Internet.

[0010] The information processing device 1 is an information processing device capable of various information processing and transmitting and receiving information, such as a server device or a personal computer. In this embodiment, the information processing device 1 is assumed to be a server device, and for simplicity, will be referred to as server 1 in the following description. The server 1 acquires content such as images and text, which are layout materials for a document page, from the terminal 2, and generates a layout image in which each content is arranged within the document page. In this embodiment, the server 1 calculates a score representing an evaluation value of the generated layout image using a scoring model 141 (classifier) ​​that has been trained on the layout of an existing document page by machine learning, as will be described later. The server 1 returns (outputs) information about the generated layout image to the terminal 2 according to the calculated score.

[0011] Terminal 2 is a terminal device used by each user of this system, such as a personal computer, smartphone, tablet terminal, etc. Users of this system are, for example, publishers that create magazines, books, etc., and use this system to create document page layouts.

[0012] The layout image generated by the server 1 may be not only a page related to a printed matter such as a magazine or a book, but also a page layout on the Web, such as a Web page.

[0013] 2 is a block diagram showing an example configuration of the server 1. The server 1 includes a control unit 11, a main memory unit 12, a communication unit 13, and an auxiliary memory unit 14. The control unit 11 has one or more arithmetic processing devices such as a central processing unit (CPU), a micro-processing unit (MPU), a graphics processing unit (GPU), etc., and performs various information processing, control processing, etc. related to the server 1 by reading and executing a program P stored in the auxiliary memory unit 14. The main memory unit 12 is a temporary storage area such as a static random access memory (SRAM), a dynamic random access memory (DRAM), or a flash memory, and temporarily stores data required for the control unit 11 to execute arithmetic processing. The communication unit 13 is a communication module for performing processing related to communication, and transmits and receives information to and from the outside.

[0014] The auxiliary storage unit 14 is a large-capacity memory, a hard disk, or the like, and stores a program P and other data necessary for the control unit 11 to execute processing. The auxiliary storage unit 14 also stores a scoring model 141 used to calculate the score of a layout image. The scoring model 141 is a trained model (classifier) ​​for calculating the score of a layout image, and as will be described later, is a trained model generated using layout images of existing document pages as training data.

[0015] The auxiliary storage unit 14 may be an external storage device connected to the server 1. The server 1 may be a multi-computer consisting of multiple computers, or may be a virtual machine virtually constructed by software.

[0016] Furthermore, in this embodiment, the server 1 is not limited to the above configuration, and may include, for example, a reading unit that reads information stored in a portable storage medium, an input unit that accepts operation input, a display unit that displays images, etc.

[0017] FIG. 3 is an explanatory diagram showing an overview of this embodiment. FIG. 3 schematically shows how the server 1 generates a layout image of a document page upon receiving a request from the terminal 2. The overview of this embodiment will be described based on FIG. 3. First, the server 1 acquires data of multiple contents to be arranged on the document page from the terminal 2 and accepts a request to generate a layout image. For example, the server 1 acquires document data in which text, images, etc. to be arranged on the document page are structured, such as an XML (Extensible Markup Language) file. Note that the content data acquired from the terminal 2 is not limited to structured data, and may be unstructured data.

[0018] When acquiring content data from terminal 2, server 1 also accepts input specifying the number of pages of the document to be created. As will be described later, server 1 allocates and arranges the acquired multiple contents to each page, and generates a layout image for each page. The process of generating a layout image for multiple pages will be described in detail later.

[0019] By executing the program P, the server 1 has the functions of a layout information generation unit 111, a layout image generation unit 112, and a layout evaluation unit 113. The layout information generation unit 111 generates layout information for arranging multiple contents acquired from the terminal 2 within a predetermined area corresponding to a document page. Specifically, the layout information generation unit 111 establishes minimum regulations for the document layout (aligning the grid, ensuring that each content does not overlap, etc.), and then randomly determines the placement coordinates for arranging each content within the predetermined area or the size of each content (the size it occupies within the page) to the extent that it does not violate the regulations.

[0020] The layout information generation unit 111 randomly determines the placement coordinates, etc. of each content and generates multiple patterns of layout information. Fig. 3 illustrates how N types of layout information are generated. The layout information generation unit 111 generates N types of layout information with different layouts by making the coordinates, size, etc. of each content different in each layout pattern.

[0021] The layout image generation unit 112 generates a layout image in which each content is arranged in a predetermined area in accordance with the layout information generated by the layout information generation unit 111. Specifically, the layout image generation unit 112 generates N different layout images corresponding to the N different pieces of layout information generated by the layout information generation unit 111.

[0022] The layout evaluation unit 113 calculates a score that evaluates the likelihood of the layout for each of the N generated layout images. Specifically, the layout evaluation unit 113 calculates a score for each of the N generated layout images using a scoring model 141 (classifier) ​​that has learned the layout of existing document pages. The scoring model 141 will be described in detail later.

[0023] The server 1 identifies the ranking of the N layout images based on the scores calculated above. The server 1 outputs information about the generated layout images to the terminal 2 according to the identified ranking. For example, the server 1 identifies the top M predetermined layout images and outputs layout information (arrangement information) corresponding to the top M layout images to the terminal 2. The terminal 2 imports the layout information acquired from the server 1 into predetermined document editing software, and the user creates the final document page.

[0024] For example, the server 1 may output information about layout images that have a score equal to or greater than a threshold. Alternatively, the server 1 may output information about all generated layout images, and also present (output) scores and rankings. In this way, the server 1 only needs to be able to present information about layout images according to the calculated scores (evaluations), and the manner in which it presents information is not limited to being based on rankings.

[0025] Fig. 4 is an explanatory diagram of the layout learning process. In this embodiment, the server 1 calculates scores using a neural network constructed by deep learning, specifically a CNN (Convolution Neural Network), as the scoring model 141. Fig. 4 conceptually illustrates how deep learning is performed using layout images of existing document pages as training data to construct (generate) the scoring model 141. The layout learning process for constructing the scoring model 141 will be described with reference to Fig. 4.

[0026] In this embodiment, the scoring model 141 is described as being a CNN, but the scoring model 141 may also be other trained models such as other neural networks, SVMs (Support Vector Machines), Bayesian networks, decision trees, etc.

[0027] In this embodiment, the server 1 generates the scoring model 141 using a rank learning technique. Rank learning is a learning technique that learns the ranking of a dataset. Known examples of neural networks that perform rank learning by deep learning include DeepLank and SiameseNet. Rank learning is a well-known learning technique, so a detailed description thereof will be omitted.

[0028] For example, the server 1 acquires a manually created layout image of an existing document page from the terminal 2 and uses it as training data for learning. For convenience, in the following explanation, this layout image will be referred to as an "existing layout image." For example, the server 1 uses the existing layout image as correct answer data with a score of "1."

[0029] Furthermore, the server 1 randomly rearranges the placement coordinates of the content placed in the existing layout image, and generates multiple layout images with the content placement changed. For convenience in the following explanation, these layout images will be referred to as "fake layout images." The server 1 uses the fake layout images as incorrect data with a score of "0."

[0030] The server 1 inputs an existing layout image and a fake layout image in which the content arrangement has been changed from the existing layout image into a neural network and performs rank learning. Specifically, the server 1 performs learning so that the score of the existing layout image will be higher than the score of the fake layout image. The server 1 compares the score of the existing layout image with the scores of each of the multiple fake layout images generated above and performs learning so that the score of the existing layout image will be higher than the scores of any of the multiple fake layout images. In this way, the server 1 generates a scoring model 141. The server 1 calculates the score of the layout image using the generated scoring model 141.

[0031] In the above, the server 1 generates part of the training data (fake layout images) by itself, but the training data may be entirely created manually. Also, the training data may be manually labeled with correct values ​​such as scores and rankings.

[0032] Furthermore, the processing entity (server 1) that performs the layout learning process and the layout generation process does not have to be the same.

[0033] Fig. 5 is an explanatory diagram of the layout generation process. Fig. 5 conceptually illustrates the process of generating a layout image. As described above, the server 1 acquires structured data of content to be arranged on a document page from the terminal 2. Furthermore, when acquiring the content data, the server 1 also acquires the number of pages K of the document specified by the user from the terminal 2.

[0034] The server 1 allocates and arranges the multiple contents acquired from the terminal 2 to each page so as to achieve the specified number of pages K, thereby generating K layout images. The server 1 randomly determines the page and arrangement coordinates on which to arrange each content, and generates layout images for K pages, generating a group of N layout images, as shown in Fig. 5. That is, the server 1 ultimately generates K x N layout images.

[0035] The server 1 inputs each generated layout image into a scoring model 141 and calculates a score for the layout image. In this case, the server 1 prepares a different scoring model 141 for each page of the input layout image, for example, and inputs the layout image into the scoring model 141 corresponding to each page to calculate the score. For example, the server 1 may prepare K scoring models 141 according to the number of pages, or may prepare scoring models 141 according to the classification of pages in a document (for example, in the case of a magazine, classification such as cover, table of contents page, article page, advertisement page, etc.). During learning, the server 1 performs learning using the layout image corresponding to each page as training data and generates a scoring model 141 corresponding to each page.

[0036] For each of the N layout image groups, the server 1 calculates the score of the layout image of each page using the scoring model 141 corresponding to each page. The server 1 then adds up the scores of all pages to calculate a design score. The server 1 ranks the N layout image groups based on the calculated design scores. In this way, the server 1 obtains the rankings of the generated layout images.

[0037] As already explained, the server 1 outputs to the terminal 2 information on the layout image group having the top M design scores out of the N layout image groups.

[0038] As described above, the server 1 evaluates layout images using the scoring model 141 that has learned document layouts, and presents highly evaluated layout images to the user. This saves the user the trouble of deciding on a document layout, and improves the efficiency of document creation.

[0039] FIG. 6 is a flowchart showing an example of the processing procedure of the layout learning processing. The processing contents of the layout learning processing will be described with reference to FIG. 6. The control unit 11 of the server 1 acquires an existing layout image to be used as training data (step S11). The existing layout image is a layout image of a document page created manually. The control unit 11 generates a fake layout image by rearranging each piece of content included in the existing layout image acquired in step S11 (step S12). For example, the control unit 11 randomly rearranges multiple pieces of content included in the existing layout image acquired in step S11 to generate multiple fake layout images.

[0040] Based on the existing layout image acquired in step S11 and the false layout image generated in step S12, control unit 11 generates scoring model 141 that has been trained to output a score (evaluation) of the layout image when the layout image is input (step S13). As described above, control unit 11 performs training using a rank learning method to generate scoring model 141 related to CNN. Specifically, control unit 11 performs training by using the layout image acquired in step S11 as correct data and the false layout image generated in step S12 as incorrect data. Control unit 11 then ends the series of processes.

[0041] FIG. 7 is a flowchart showing an example of the processing procedure of the layout generation processing. The processing details of the layout generation processing will be described with reference to FIG. 7. The control unit 11 of the server 1 acquires, from the terminal 2, a plurality of contents to be arranged on document pages and a specified number of pages (step S31). The control unit 11 generates layout information for allocating and arranging the plurality of contents to each page according to the specified number of pages (step S32). Specifically, the control unit 11 randomly determines the arrangement coordinates for arranging each content within a predetermined area, and generates multiple patterns of layout information. The control unit 11 generates layout images of each page in which the content is arranged within the predetermined area according to the generated layout information (step S33). Specifically, the control unit 11 generates multiple patterns of layout images according to the multiple patterns of layout information generated in step S32.

[0042] Control unit 11 calculates a score for the layout image of each page generated in step S33 using scoring model 141 that has been trained on multiple layout images (step S34). Specifically, control unit 11 calculates a score for each page using a different scoring model 141 for each page. Control unit 11 calculates a score for each of the multiple patterns of layout images generated in step S33.

[0043] The control unit 11 adds up the scores of the layout images of each page and calculates a design score for each of the multiple patterns (step S35). The control unit 11 ranks the layout images of each pattern according to the calculated design score, and outputs layout information related to the top-ranked layout images to the terminal 2 (step S36). The control unit 11 then ends the series of processes.

[0044] In the above, the scoring model 141 is assumed to output a score (evaluation value) of the layout image, but it may also be configured to output only a classification result indicating the suitability of the layout image. In other words, the server 1 only needs to be able to acquire an evaluation of the layout image using the trained model, and the acquired evaluation is not limited to a continuous probability value.

[0045] As described above, according to the first embodiment, by outputting information about a layout image according to the score calculated using scoring model 141, it is possible to present an appropriate layout to the user.

[0046] Furthermore, according to the first embodiment, the ranking of multiple layout images is determined using the scoring model 141, and the layout images are output according to the determined ranking, thereby making it possible to present a more appropriate layout to the user.

[0047] Furthermore, according to the first embodiment, by calculating the score of the layout image using a different scoring model 141 for each page, it is possible to appropriately evaluate the layout image in consideration of the characteristics of each page.

[0048] (Embodiment 2) In this embodiment, a form will be described in which a user specifies layout conditions in advance, and a layout according to the specified conditions is output. Note that the same reference numerals will be used to designate the same parts as in the first embodiment, and a description thereof will be omitted. Fig. 8 is an explanatory diagram showing an outline of the second embodiment. The contents of Fig. 8 are almost the same as those of Fig. 3, and therefore a description of the common matters will be omitted. The outline of this embodiment will be described based on Fig. 8.

[0049] In this embodiment, when the server 1 receives a request to generate a layout image from the terminal 2, it acquires condition information that specifies the conditions for arranging each content on a document page, in addition to the data of each content and the specified number of pages. The condition information is a layout condition arbitrarily specified by the user creating the document, and is prior knowledge information about the content that is known only to a human (user) and is necessary for determining the layout.

[0050] In this embodiment, the server 1 accepts input of condition information for images among content such as images and text. For example, the server 1 accepts input of the importance of each image and the order in which each image is to be arranged on a document page. The importance is input, for example, as a rank in multiple stages. The order is input, for example, as an ascending numerical order.

[0051] For example, if the document is a magazine, the image is often laid out so that the size of the image increases with increasing importance, such as double-page spread size, full page size, half page size, etc. Therefore, the server 1 acquires the importance of the image as condition information and determines the size of the image according to the importance.

[0052] Furthermore, when multiple images are related to each other, the order in which images should be placed first on a page is often determined based on the image content. For example, when laying out a full-image photo and detailed photos of a subject on a page, it is generally better to place the full-image photo first. Therefore, the server 1 acquires the image placement order as condition information.

[0053] As in the first embodiment, the server 1 generates a layout image in which each content is randomly arranged, inputs the layout image to the scoring model 141, and calculates a design score. Furthermore, in this embodiment, the server 1 corrects the calculated design score based on the above-mentioned condition information. That is, the server 1 changes the evaluation of the layout image in accordance with the condition information.

[0054] Specifically, the server 1 compares the size, arrangement order, etc. of the content (images) in the layout image of each page with the importance, arrangement order, etc. specified in the condition information, and determines whether each content is arranged in a size, order, etc. that violates the condition information. The server 1 then counts the number of contents that are arranged in violation of the condition information. In other words, the server 1 counts the number of violations that violate the layout conditions specified by the user.

[0055] The server 1 multiplies the counted number of contents (number of violations) by a predetermined coefficient and subtracts the result from the design score. In other words, the server 1 imposes a penalty according to the number of violations. The server 1 ranks the images based on the design score finally calculated by the above process, and outputs information on the top layout images to the terminal 2.

[0056] As described above, the server 1 acquires in advance condition information that defines the content layout conditions, and evaluates the layout image based on the condition information, thereby making it possible to present a layout image that the user desires.

[0057] In the above, the condition information is used only for evaluating the layout image, but the present embodiment is not limited to this, and the server 1 may use the condition information for generating the layout image. For example, the server 1 determines the image size, etc., by referring to the importance and arrangement order of the images specified in the condition information, and arranges them on each page. Even in this case, the same effect as above can be achieved.

[0058] 9 is a flowchart showing an example of a processing procedure for a layout generation process according to the second embodiment. The control unit 11 of the server 1 acquires, from the terminal 2, condition information that defines the content placement conditions, in addition to a plurality of contents to be placed on a document page and a specified number of pages (step S201). The condition information is information that defines the layout conditions when placing each content on a page, such as the importance of the content, the placement order, etc., as described above. The control unit 11 proceeds to step S32.

[0059] After calculating the design score of the layout image (step S34), the control unit 11 corrects (changes) the design score based on the condition information acquired in step S201 (step S202). Specifically, the control unit 11 compares the size, arrangement order, etc. of each piece of content in the generated layout image with the importance, arrangement order, etc. of the content specified in the condition information, and determines whether each piece of content is arranged in a size, order, etc. that violates the condition information. The control unit 11 counts the number of pieces of content that are arranged in violation of the condition information, and subtracts a predetermined value from the design score according to the counted number of pieces of content. The control unit 11 proceeds to step S36.

[0060] As described above, according to the second embodiment, a layout image is evaluated in accordance with the condition information specified by the user, so that a more appropriate layout can be presented.

[0061] Furthermore, according to the second embodiment, it is also possible to generate a layout image based on condition information specified by the user.

[0062] (Embodiment 3) FIG. 10 is a functional block diagram showing the operation of the server 1 in the above-described embodiment. The control unit 11 executes the program P, causing the server 1 to operate as follows: The acquisition unit 101 acquires a plurality of contents. The generation unit 102 generates a layout image in which the plurality of contents are arranged in a predetermined area. The evaluation unit 103 uses a classifier that has trained the plurality of layout images to acquire an evaluation of the generated layout image. The output unit 104 outputs the evaluation result.

[0063] The third embodiment is as described above, and other aspects are the same as those of the first and second embodiments, so the corresponding parts are given the same reference numerals and detailed description thereof will be omitted.

[0064] (Fourth embodiment) In the above-described embodiment, an appropriate layout can be provided to the user. When determining a layout, particularly in the initial stage of consideration, it may be desirable to have a wide variety of layouts. This point will be described below.

[0065] The layout evaluation unit 113 calculates a score that evaluates the likelihood of the layout for each of the N generated layout images (similar to the first to third embodiments), and also calculates a diversity score that represents the diversity for each of the N generated layout images, and calculates a total score based on both scores. The method for calculating the total score will be described below.

[0066] FIG. 11 is an explanatory diagram showing the main parts of the scoring model 141. The scoring model 141 can be, for example, a CNN, and has fully connected layers 141a and 141b. Note that the number of fully connected layers is not limited to the example of FIG. 11. In the fully connected layers 141a and 141b, both the input and output are vectors. Since the fully connected layer 141b is located before the output layer, it combines the features of the input layout image and corresponds to an identification unit for classifying layouts. In the fourth embodiment, the vectors in the fully connected layer 141b are used as identification indices for identifying the features of the layout image. Note that other fully connected layers may also be used.

[0067] FIG. 12 is an explanatory diagram showing an example of calculation of the overall score. For convenience, N types of layout images are represented as G1, G2, G3, ..., GN. In the first to third embodiments, the calculated design score is represented as STi, where i = 1 to M. For example, the design score of layout image Gi is STi. The diversity score between layout image Gi and layout image Gj is represented as SDi,j (or SD(i,j)). Here, j = 1 to M, and i <jとする。

[0068] For example, in the example of FIG. 12, the design score of layout image G1 is ST1, and the diversity scores between layout images G2, G3, ..., GN are SD(1, 2), SD(1, 3), ..., SD(1, N). The design score of layout image G2 is ST2, and the diversity scores between layout images G1, G3, ..., GN are SD(1, 2), SD(2, 3), ..., SD(2, N). Note that SD(2, 1) is equal to SD(1, 2), so the notation SD(1, 2) is used consistently. The same applies to the other layout images.

[0069] As shown in FIG. 11, when layout image Gi of N layout images is input to scoring model 141, the vector of fully connected layer 141b is defined as vector di, and when layout image Gj is input to scoring model 141, the vector of fully connected layer 141b is defined as vector dj. Here, i ≠ j. The diversity score SDi,j between layout images Gi and Gj can be set to 1 minus the cosine similarity between vectors di and dj. If the layouts of layout images Gi and Gj are similar, the cosine similarity between vectors di and dj approaches 1, and therefore the diversity score SDi,j approaches 0. On the other hand, if the layouts of layout images Gi and Gj are dissimilar, the cosine similarity between vectors di and dj approaches 0, and therefore the diversity score SDi,j approaches 1.

[0070] The overall score S can be calculated using the formula S = Σ(STi + λ·SDi, j), where Σ is the sum from 1 to N for i and j. λ is a weighting parameter (weighting coefficient) that can be set to a desired value.

[0071] FIG. 13 is an explanatory diagram showing an overview of the fourth embodiment. The layout evaluation unit 113 functions as a calculation unit and calculates the similarity between any two layout images among a plurality of layout images. The layout evaluation unit 113 calculates an overall score using a score for evaluating design and a score for evaluating diversity, and determines a selection set (subset) that maximizes the overall score. In other words, the overall score for all M layout images that make up the selection set is maximized. More specifically, the layout evaluation unit 113 calculates an overall score by taking into consideration the diversity score in addition to the design score described in the first to third embodiments, and can evaluate the layout images.

[0072] The design score STi indicates the appropriateness of the layout image, and is an index that indicates, for example, the likeness of the layout image. On the other hand, the diversity score SDi,j indicates the dissimilarity or the high degree of variation. By using the overall score S, it is possible to output layout images (top M) that are highly likeness of the layout image, appropriateness, and diverse, as illustrated schematically in Figure 13.

[0073] FIG. 14 is a flowchart showing an example of a processing procedure for layout generation processing according to the fourth embodiment. The processing details of the layout generation processing will be described with reference to FIG. 14. The control unit 11 of the server 1 acquires, from the terminal 2, a plurality of contents to be arranged on document pages and a specified number of pages (step S31). The control unit 11 generates layout information for allocating and arranging a plurality of contents to each page according to the specified number of pages (step S32). Specifically, the control unit 11 randomly determines arrangement coordinates for arranging each content within a predetermined area, and generates multiple patterns of layout information. The control unit 11 generates layout images of each page in which the content is arranged within the predetermined area according to the generated layout information (step S33). Specifically, the control unit 11 generates multiple patterns of layout images according to the multiple patterns of layout information generated in step S32.

[0074] Control unit 11 calculates a score for the layout image of each page generated in step S33 using scoring model 141 that has been trained on multiple layout images (step S34). Specifically, control unit 11 calculates a score for each page using a different scoring model 141 for each page. Control unit 11 calculates a score for each of the multiple patterns of layout images generated in step S33.

[0075] The control unit 11 adds up the scores of the layout images of each page and calculates a design score for each of the multiple patterns (step S35). The control unit 11 calculates a diversity score for the layout image of each page (S211). Specifically, the control unit 11 can calculate the diversity score for each page using a different scoring model 141 for each page. The control unit 11 calculates a diversity score for each of the multiple patterns of layout images generated in step S33.

[0076] The control unit 11 adds up the diversity scores of the layout images of each page and calculates a diversity score for each of the multiple patterns (step S212). The control unit 11 calculates a total score for each of the multiple patterns based on the design score and the diversity score (S213). The control unit 11 ranks the layout images of each pattern according to the calculated total score, and outputs M sets (combinations) with the highest total score to the terminal 2 (step S36). The control unit 11 ends the series of processes.

[0077] As described above, according to embodiment 4, by outputting layout image information according to the score calculated using scoring model 141, it is possible to present to the user an appropriate layout (a layout that is highly representative of a work of art) and a wide variety of layouts.

[0078] The embodiments disclosed herein are to be considered as illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0079] 1. Server (information processing device) 11 Control section 12 Main memory 13 Communications Department 14 Auxiliary storage P Program 141 Scoring Model 141a, 141b fully connected layer 2. Devices

Claims

1. an acquisition unit that acquires a plurality of contents; a generation unit that generates a layout image in which the plurality of contents are arranged in a predetermined area; an evaluation unit that acquires an evaluation of a generated layout image using a classifier that has been trained on a plurality of layout images; an output unit that outputs the evaluation result; the acquiring unit acquires the plurality of contents and the number of pages of the layout image; the generating unit generates a plurality of layout images in which the plurality of contents are allocated to and arranged on each page according to the number of pages; The evaluation unit obtains an evaluation of each of the plurality of layout images using the classifier that differs depending on the page.

1. An information processing device comprising:

2. the generation unit generates a plurality of layout images in which the plurality of contents are arranged differently; the evaluation unit obtains a ranking of the plurality of layout images using the classifier; The output unit outputs the ranking.

2. The information processing apparatus according to claim 1, wherein:

3. the acquisition unit acquires condition information that defines a placement condition for the content; The evaluation unit changes the evaluation obtained from the classifier based on the condition information.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. the acquisition unit acquires condition information that defines a placement condition for the content; The generating unit generates the layout image based on the condition information.

4. The information processing device according to claim 1, wherein the information processing device is a computer.

5. a calculation unit that calculates a similarity between any two layout images among the plurality of layout images; 5. The information processing apparatus according to claim 1, wherein the evaluation unit evaluates the plurality of layout images using the evaluation value by the classifier and the similarity calculated by the calculation unit.

6. Acquire multiple contents and the number of pages of the layout image, generating a plurality of layout images in which the plurality of contents are allocated to each page according to the number of pages and arranged in a predetermined area; acquiring an evaluation of each of the plurality of layout images generated using different trained classifiers according to the pages; Output the evaluation results An information processing method characterized by causing a computer to execute processing.

7. Acquire multiple contents and the number of pages of the layout image, generating a plurality of layout images in which the plurality of contents are allocated to each page according to the number of pages and arranged in a predetermined area; acquiring an evaluation of each of the plurality of layout images generated using different trained classifiers according to the pages; Output the evaluation results A program that causes a computer to execute a process.

Images