Image search system and image search method

Abstract

To provide an image search system, etc., that identifies an image from linguistic descriptions of its visual features and generates appropriate response content. [Solution] The image search system comprises a preprocessing unit, an acquisition unit, an identification unit, and an output unit. The preprocessing unit generates at least one image description describing the visual features of each of a plurality of images and stores the image descriptions associated with each of the plurality of images. The acquisition unit acquires input information including linguistic expressions about the visual features of the images. The identification unit matches the corresponding image descriptions based on the input information and identifies the images associated with the image descriptions. The output unit generates and outputs the response content for the images. Artificial intelligence utilizing cross-modal technology can be used to generate the image descriptions.

Description

Technical Field

[0001] The present invention relates to an image search system and an image search method.

Background Art

[0002] In a general image search system, when a user of the image search system searches for an image, an icon, a figure, etc., it is required to directly input the reference image into the system. However, when the reference image of the user is a printed matter or a display by another terminal and cannot be captured, it is difficult to input the image into the image search system. Patent Document 1 discloses a similar image search device that prevents a decrease in search accuracy.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The similar image search device described in Patent Document 1 not only uses the similarity of image feature amounts with a designated base image, but also uses the narrowing down of similar images based on the similarity of text feature amounts related to the text associated with the designated base image. Thereby, the similar image search device prevents a decrease in search accuracy when performing similar image search using an image as a key. However, the similar image search device described in Patent Document 1 searches for similar images from a base image and does not describe searching for an image from the linguistic expression of the image by the user. Therefore, it is not possible to generate an answer from a question including the linguistic expression of the image.

[0005] In view of the above problems, the present disclosure provides an image search system or the like that identifies an image from the linguistic expression of the visual features of the image and preferably generates answer content. [Means for solving the problem]

[0006] An image retrieval system according to one aspect of the present disclosure comprises a preprocessing unit, an acquisition unit, a identification unit, and an output unit. The preprocessing unit generates at least one image description describing the visual features of each of a plurality of images and stores the image descriptions associated with each of the plurality of images. The acquisition unit acquires input information including linguistic expressions about the visual features of the images. The identification unit matches the corresponding image descriptions based on the input information and identifies the images associated with the image descriptions. The output unit generates and outputs response content for the images.

[0007] The above image search system may further include a preprocessing unit that extracts image attributes related to visual features from each of multiple images, generates an image description that includes the image attributes, and stores the image attributes in association with each of the multiple images. The image search system may further include an attribute extraction unit that extracts search attributes related to visual features from input information, and a specification unit that further matches the corresponding image description based on the search attributes.

[0008] The above image search system may be an icon, sign, symbol, or mark, and the input information may include a question about the icon, sign, symbol, or mark, with an attribute extraction unit extracting search attributes from the question. The image search system may also have an identification unit that identifies image attributes that match the search attributes, identify images associated with image descriptions containing the identified image attributes, and an output unit that generates the answer to the question as the answer content.

[0009] The above image search system may have a preprocessing unit that generates prompts for image generation based on images having image attributes. The image search system may also have a identification unit that generates search images based on prompts corresponding to search attributes and identifies corresponding images based on the search images.

[0010] An image retrieval method according to one aspect of this disclosure involves a computer performing the following processes: The computer generates at least one image description describing the visual features of each of a group of images and stores the image descriptions associated with each of the group of images. The computer obtains input information containing linguistic descriptions of the visual features of the images. Based on the input information, the computer matches the corresponding image descriptions and identifies the images associated with the image descriptions. The computer generates and outputs a response for the images. [Effects of the Invention]

[0011] According to this disclosure, an image search system and an image search method can be provided that identify an image from a linguistic expression of the visual features of the image and suitably generate response content. [Brief explanation of the drawing]

[0012] [Figure 1] This is a block diagram of the image search system according to Embodiment 1. [Figure 2] This is a flowchart of the image search method according to Embodiment 1. [Figure 3] This is a block diagram of the image search system according to Embodiment 2. [Figure 4] This is a flowchart of the image search method according to Embodiment 2. [Figure 5] This is a flowchart of the image search method according to Embodiment 3. [Figure 6] This is a block diagram illustrating the hardware configuration of a computer. [Modes for carrying out the invention]

[0013] The present invention will be described below through embodiments of the invention, but the invention claimed is not limited to the following embodiments. Furthermore, not all of the configurations described in the embodiments are necessarily essential as means of solving the problem. For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. In each drawing, the same elements are denoted by the same reference numerals, and redundant explanations have been omitted where necessary.

[0014] <Embodiment 1> Referring to Figure 1, the image search system 10 according to Embodiment 1 will be described. Figure 1 is a block diagram of the image search system 10 according to Embodiment 1. The image search system 10 identifies an image from a linguistic expression of the visual features of the image and generates a response for the identified image. Here, the image is, for example, an icon, sign, symbol, or mark. The image is not limited to these and may also be a photograph, etc. Visual features are the visual characteristics of the image, such as the shape, size, color, or orientation of the image. The image search system 10 comprises a preprocessing unit 101, an acquisition unit 102, an identification unit 103, and an output unit 104.

[0015] The preprocessing unit 101 generates at least one image description for each of the multiple images, describing its visual features. The preprocessing unit 101 may also describe the visual features by dividing them into multiple image descriptions. An image description is a sentence that describes an image. An image description is also called a caption. Image descriptions are generated using, for example, generative AI (Artificial Intelligence). Here, the generative AI is a multimodal model based on large-scale language generation technology.

[0016] A multimodal model is an AI model that utilizes deep learning, similar to a Large Language Model (LLM), to process not only text but also images, audio, or video. In this context, the multimodal model is trained on at least images and text, and can convert between them.

[0017] The preprocessing unit 101 includes a storage means (not shown) and stores image description texts associated with each of a plurality of images. For example, the preprocessing unit 101 stores a list associating images with image description texts as a DB (database). Also, the preprocessing unit 101 may store the source or identification information of the image instead of storing the image itself. Note that the preprocessing unit 101 may also store the meaning indicated by the image in association with the image.

[0018] The acquisition unit 102 acquires input information including a linguistic expression about the visual features of an image. The acquisition unit 102 acquires the input information in text format. For example, the acquisition unit 102 receives a user's question via an interface such as a communication device, a microphone, or a touch panel, and acquires it as input information in text format. Here, the question is, for example, about the meaning indicated by the image that the user refers to.

[0019] The specifying unit 103 collates the corresponding image description text based on the input information and specifies the image associated with the image description text. For example, the specifying unit 103 compares the linguistic expression about the visual features included in the input information with the image description text, and collates the image description text corresponding to the linguistic expression based on the similarity. The specifying unit 103 may extract specific words included in the linguistic expression about the visual features and collate the corresponding image description text based on the specific words. For example, the specifying unit 103 compares the linguistic expression with the image description text using an algorithm or AI.

[0020] The output unit 104 generates and outputs a response content for the specified image. For example, the output unit 104 includes image data, the source of the image, an explanation of the image, or a generated response, etc. in the response content. That is, the response content may be the data of the specified image or a response based on the image. Also, the output unit 104 may calculate the degree of match between the input information and the image description text and include it in the response content as a response confidence level.

[0021] According to this, the image search system 10 can obtain input information from the user in text format and identify images corresponding to the visual features indicated by the input information. Therefore, the image search system 10 can identify the image the user is looking for and generate a response based on the identified image.

[0022] Figure 2 is a flowchart of the image search method according to Embodiment 1. The flowchart of the image search method according to the image search system 10 includes steps S11 to S16.

[0023] In step S11, the preprocessing unit 101 of the image search system 10 generates at least one image description for each of the multiple images, describing its visual features. Here, the image is, for example, a mark, icon, logo, or symbol. The visual features are the shape, size, color, or any characters or symbols contained in the image. In step S12, the preprocessing unit 101 stores the image description associated with each of the multiple images. Steps S11 and S12 constitute preprocessing. Therefore, when the image search system 10 executes the image search method according to Embodiment 1 multiple times, it may omit the processing in steps S11 and S12 from the second time onward.

[0024] In step S13, the acquisition unit 102 acquires input information in text format, which includes a linguistic representation of the visual features of the image the user is referring to. Here, the input information is, for example, a question about the meaning of the image. The acquisition unit 102 accepts input from the user, for example, through communication, voice, or typing, and acquires it as input information in text format.

[0025] In step S14, the identification unit 103 matches the corresponding image description based on the input information. For example, the identification unit 103 compares the linguistic expression of the visual features indicated by the input information with a plurality of stored image descriptions and matches the image description corresponding to the linguistic expression of the visual features based on the similarity. The matching method of the identification unit 103 is not limited to this. In step S15, the identification unit 103 identifies the image associated with the image description matched in step S14.

[0026] In step S16, the output unit 104 generates and outputs the response content for the identified image. The response content is, for example, the answer to the question which is the input information. The output unit 104 may output the identified image. The output unit 104 may output the response content using visual or auditory presentation means. The output unit 104 is not limited to these methods, and may output the response content by transmitting it to an external device.

[0027] As explained above, the image search system 10 identifies images associated with image descriptions by matching them with the linguistic expressions of the visual features contained in the acquired input information. This allows the image search system 10 to identify images corresponding to the input information and generate appropriate responses for those images. Therefore, users of the image search system 10 can obtain search results for images without inputting any images into the system.

[0028] The image search system 10 may also have a processor and a storage device, although these are not shown in the diagram. The storage device of the image search system 10 may include, for example, a non-volatile memory such as flash memory or an SSD (Solid State Drive). In this case, the storage device stores a computer program (hereinafter also referred to simply as a program) for executing the above-described method. The processor loads the computer program from the storage device into a buffer memory such as DRAM (Dynamic Random Access Memory) and executes the program.

[0029] Each component of the image search system 10 may be implemented with dedicated hardware. Furthermore, some or all of each component may be implemented by general-purpose or dedicated circuits, processors, etc., or combinations thereof. These may be implemented by a single chip or by multiple chips connected via a bus. Some or all of each component of each device may be implemented by a combination of the aforementioned circuits, etc., and programs. Processors include CPUs (Central Processing Units), GPUs (Graphics Processing Units), FPGAs (Field-Programmable Gate Arrays), etc. Also, at least a portion of the processing performed by the image search system 10 may be provided as SaaS (Software as a Service). The descriptions of the configurations described herein may also apply to other systems described below in this disclosure.

[0030] Furthermore, the image search system 10 may be implemented using hardware mounted on a mobile device, and some of the hardware may be installed elsewhere rather than on the mobile device. In this case, the device installed elsewhere (e.g., a server) and the device installed on the mobile device are connected via a communication network.

[0031] <Embodiment 2> Figure 3 is a block diagram of the image search system 11 according to Embodiment 2. The image search system 11 extracts search attributes related to visual features from the question contained in the input information and further matches the image description text based on the search attributes. Here, the question relates to an icon, sign, symbol, or mark. The image search system 11 according to Embodiment 2 has some of the same configuration as the image search system 10 in Figure 1. Therefore, a description of the configuration of the image search system 11, which performs the same processing as the image search system 10, will be omitted. The image search system 11 comprises a pre-processing unit 111, an acquisition unit 102, an attribute extraction unit 113, a identification unit 114, and an output unit 115.

[0032] The preprocessing unit 111 extracts image attributes related to visual features from each of the multiple images. Here, the images are icons, signs, symbols, or marks. Image attributes are identifiers that indicate the visual features of the image. Image attributes include, for example, the color, background color, or orientation of the image, or the number of symbols, characters, or components contained in the image.

[0033] The preprocessor 111 generates an image description that includes the image attributes. Specifically, the preprocessor 111 generates a suitable image description by constructing prompts that include the content of the image attributes and inputting them into a multimodal model. The preprocessor 111 performs prompt tuning, for example. Prompt tuning is a method of optimizing prompts to obtain the desired output through learning, and does not require retraining or adjustment of the generating AI. A prompt is an instruction that prompts the generating AI to produce the desired result. Here, the preprocessor 111 may generate a single image description that indicates multiple image attributes, or it may generate multiple image descriptions that indicate each of the multiple image attributes.

[0034] The preprocessing unit 111 further stores image attributes associated with each of the multiple images. For example, the preprocessing unit 111 stores the image attributes as a database by associating them with a list of images and their image descriptions. Alternatively, the preprocessing unit 111 may generate and store a separate list of images and their image attributes.

[0035] The attribute extraction unit 113 extracts search attributes related to visual features from the input information. Search attributes correspond to image attributes and are identifiers that indicate the visual features of the image the user is referring to. Here, the attribute extraction unit 113 extracts search attributes from the questions included in the input information. That is, the attribute extraction unit 113 extracts search attributes from the linguistic expressions about the visual features of the image in the questions.

[0036] The identification unit 114 further matches the corresponding image description based on the search attributes extracted by the attribute extraction unit 113. Specifically, the identification unit 114 determines candidate image descriptions based on the search attributes and matches the corresponding image description from among the candidates based on the input information. In other words, the identification unit 114 identifies image attributes that match the search attributes extracted from the question and identifies images associated with image descriptions that contain the identified image attributes. As a result, the image search system 11 can suitably identify image descriptions and images corresponding to the input information by limiting the candidates in advance based on the search attributes.

[0037] The output unit 115 generates and outputs the answer to the question as the answer content. The output unit 115 may be equipped with a display means (not shown), such as a monitor or speaker, to present the answer content to the user visually or audibly. The output unit 115 is not limited to this, and may also output the answer content by transmitting it to an external device.

[0038] Specifically, the image search system 11 installed in the vehicle accepts questions about the interior parts and meters of the passenger car. Here, the questions may ask for the names or meanings of warning lights or icons, equipment or parts, or buttons on the in-car meters. The questions may also ask for the names or meanings of traffic signs, traffic signals, or store logos. Furthermore, the questions may ask for the meanings of arrows, abbreviations, or POI (Point of Interest) icons in the in-car navigation system or map application.

[0039] Figure 4 is a flowchart of the image search method according to Embodiment 2. The flowchart of the image search method according to the image search system 11 includes steps S21 to S28.

[0040] In step S21, the preprocessor 111 extracts image attributes related to visual features from each of the multiple images. Here, the images are icons, signs, symbols, or marks. Image attributes include, for example, the color, background color, or orientation of the image, or the number of symbols, characters, or components contained in the image. In step S22, the preprocessor 111 generates an image description for each of the multiple images, which includes the image attributes in its content. The preprocessor 111 causes the image description to include the content of the image attributes, for example, by performing prompt tuning. In step S23, the preprocessor 111 stores the image attributes and image description, associating them with each of the multiple images.

[0041] Steps S21 to S23 constitute preprocessing. Therefore, when the image search system 11 executes the image search method according to Embodiment 2 multiple times, it may omit the processing in steps S21 to S23 from the second time onward.

[0042] In step S24, the acquisition unit 102 acquires input information including linguistic representations of visual features of the image. Here, the input information includes questions about icons, signs, symbols, or marks. The input information is acquired in text format. The acquisition unit 102 acquires input information, for example, through communication, voice, or typing by the user via an interface. Next, in step S25, the attribute extraction unit 113 extracts search attributes related to visual features from the input information. The search attributes correspond to the image attributes extracted in step S21.

[0043] In step S26, the identification unit 114 matches the corresponding image description based on the search attributes and input information. Then, in step S27, the identification unit 114 identifies the image associated with the image description matched in step S26. That is, the identification unit 114 identifies the image attributes that match the search attributes and identifies the image associated with the image description that contains the identified image attributes.

[0044] The identification unit 114 filters the image description text by image attributes that match the search attributes. Next, the identification unit 114 matches the question contained in the input information with the image description text based on the sequence of words and connection relationships that indicate the search attributes. In this way, the identification unit 114 can narrow down the image description texts that correspond to the search attributes. The identification unit 114 may use AI such as LLM for matching.

[0045] In step S28, the output unit 115 generates and outputs the answer content for the identified image. Here, the answer content is the answer to the question contained in the input information. The output unit 115 outputs the answer content by presenting it to the user visually or audibly. The output unit 115 is not limited to this, but may also output the answer content by transmitting it to an external device.

[0046] The image search system 11 extracts the image attributes "black," "yellow," "A," and "OFF" from an image of an indicator light related to the idle stop function, which is "a yellow illuminated mark on a black background with the letter A surrounded by an arrow and the word OFF written below it." Next, the image search system 11 generates a description for the image of the indicator light, which reads "a symbol with the letters A and OFF highlighted in yellow and black." If the user then inputs "What does the yellow illuminated A mark mean?" into the image search system 11, the image search system 11 extracts the search attributes "yellow" and "A" from the question. Subsequently, the image search system 11 identifies the image of the indicator light based on the search attributes and the question, and generates and outputs an answer such as "It indicates a malfunction in the idle stop system."

[0047] As explained above, the image search system 11 can reduce the processing required to identify an image by narrowing down the candidate image descriptions to be matched using search attributes. This allows the image search system 11 to identify an image from the linguistic representation of its visual features and generate a more favorable answer to the question. Therefore, the image search system 11 can provide a favorable answer to the question entered by the user.

[0048] Furthermore, if the generating AI in the image search system 11 is trained in a language different from the input information, the system may translate the description and input information into the primary training language of the generating AI. This allows the image search system 11 to maximize the performance of the generating AI it uses and to suitably identify images and generate answers.

[0049] Furthermore, when matching image descriptions from input information, the image search system 11 may compare multiple matching methods in advance and determine the optimal method or a list of candidate methods. This allows the image search system 11 to more effectively identify images and generate responses.

[0050] <Embodiment 3> Figure 5 is a flowchart of the image search method according to Embodiment 3. The image search method according to Embodiment 3 extracts search attributes related to visual features from input information, generates a search image which is an image used for searching based on the search attributes, and compares the stored image with the search image. The image search system that executes the image search method according to Embodiment 3 has the same configuration as the image search system 11 in Figure 3. Therefore, a description of the configuration of the image search system that executes the same processing as the image search system 11 is omitted. The image search method of the image search system according to Embodiment 3 includes steps S31 to S37.

[0051] In step S31, the preprocessing unit extracts image attributes related to visual features from each of the multiple images. In step S32, the preprocessing unit generates a prompt for image generation based on the image that has the image attributes. Steps S31 and S32 constitute preprocessing. Therefore, when the image search system executes the image search method according to Embodiment 3 multiple times, the processing in steps S31 and S32 may be omitted from the second time onward.

[0052] In step S33, the acquisition unit acquires input information that includes linguistic expressions of visual features of the image. Here, the input information includes questions about icons, signs, symbols, or marks. The input information is acquired in text format. Next, in step S34, the attribute extraction unit extracts search attributes related to visual features from the input information. The search attributes correspond to the image attributes extracted in step S31.

[0053] In step S35, the identification unit generates a search image based on a prompt for image generation corresponding to the search attribute. Next, in step S36, the identification unit identifies a corresponding image based on the search image. That is, the identification unit identifies an image attribute that matches the search attribute and identifies the image corresponding to the input information by comparing it with the search image generated by the prompt corresponding to the identified image attribute. In this way, the identification unit can improve the accuracy of image identification by applying image matching technology.

[0054] Furthermore, in step S35, the identification unit may adjust the search image to better match the user's recognition through interaction with the user. For example, the identification unit presents the generated search image using a display means such as a monitor and asks the user for confirmation. In this case, the identification unit acquires correction information indicating the correction points pointed out by the user via the acquisition unit. For example, the identification unit detects at least one of voice input and touch input via the acquisition unit and acquires the correction information. Next, the identification unit modifies the original input information so that a search image conforming to the correction information is generated. Alternatively, the identification unit generates an additional prompt to modify the search image according to the correction information and generates the modified search image. This allows the identification unit to generate a search image that is close to the user's recognition.

[0055] Furthermore, the identification unit may generate multiple search images in step S35 and present them to the user using a display means such as a monitor. In this case, the user selects the search image that is closest to their recognition among the multiple search images, and the identification unit accepts the user's selection via the acquisition unit. This allows the identification unit to suitably generate a search image that is close to the user's recognition.

[0056] In step S37, the output unit generates and outputs the answer content for the identified image. Here, the answer content is the answer to the question contained in the input information.

[0057] As described above, the image search system according to Embodiment 3 extracts search attributes related to visual features from input information including a question, and generates search images based on the search attributes. This allows the image search system to identify images corresponding to the input information using image matching technology. Therefore, the image search system can suitably generate answers for images corresponding to the input information.

[0058] It should be noted that the present invention is not limited to the embodiments described above, and can be modified as appropriate without departing from the spirit of the invention. For example, both the image search method according to Embodiment 2 and the image search method according to Embodiment 3 may be performed and the response content may be combined. The image search system may also include a user authentication unit that authenticates the user by voice, biometric information, or personal authentication, and stores each user's expressive characteristics as characteristic information. The user authentication unit may authenticate by the user selecting a registered account. Here, expressive characteristics are, for example, the user's habits or tendencies in expressing shape, color, or size, and word order.

[0059] Here, the acquisition unit and the identification unit acquire characteristic information associated with the authenticated user from the user authentication unit, and convert the user's input into a standard expression that is easy for the image search system to process based on the characteristic information. Specifically, the acquisition unit and the identification unit convert expressions such as "gray," "mouse gray," or "lead gray," which differ for each user, into the standard expression "gray" based on the characteristic information. As a result, the image search system can convert different expression methods for each user into a unified expression and process it. Therefore, the image search system can generate and output appropriate response content for the input information without being affected by the user's expression characteristics.

[0060] The output unit may also obtain characteristic information associated with the authenticated user from the user authentication unit. For example, the output unit converts the response content, expressed in a standard representation used during processing, into a representation with user-specific representation characteristics based on the characteristic information, and outputs it. This allows the image search system to output the response content in a representation familiar to the user.

[0061] <Example hardware configuration> The following describes examples of how each functional configuration of the image search system described in this disclosure can be implemented through a combination of hardware and software.

[0062] Figure 6 is a block diagram illustrating the hardware configuration of a computer. The image search system in this disclosure can realize the above-described functions using a computer 500 including the hardware configuration shown in the figure. The computer 500 may be a portable computer such as a smartphone or tablet terminal, or a stationary computer such as a PC. The computer 500 may be a dedicated computer designed to realize each device, or it may be a general-purpose computer. The computer 500 can realize the desired functions by installing a predetermined application.

[0063] Computer 500 includes a bus 502, a processor 504, memory 506, a storage device 508, an input / output interface (I / F) 510, and a network interface (I / F) 512. Bus 502 is a data transmission path for the processor 504, memory 506, storage device 508, input / output interface 510, and network interface 512 to send and receive data to and from each other. However, the method of connecting the processor 504 and other components to each other is not limited to bus connection.

[0064] Processor 504 is a processor such as a CPU, GPU, or FPGA. Memory 506 is main memory implemented using RAM (Random Access Memory), etc.

[0065] The storage device 508 is an auxiliary storage device implemented using a hard disk, SSD, memory card, or ROM (Read Only Memory). The storage device 508 stores a program for realizing a desired function. The processor 504 reads this program into memory 506 and executes it to realize each functional component of each device.

[0066] The input / output interface 510 is an interface for connecting the computer 500 to input / output devices. For example, input devices such as keyboards and output devices such as display devices are connected to the input / output interface 510. The network interface 512 is an interface for connecting the computer 500 to a network. [Explanation of Symbols]

[0067] 10, 11 Image search system 101, 111 Pre-processing section 102 Acquisition Department 103, 114 Specific part 104, 115 Output section 113 Attribute extraction part 500 Computers Bus 502 504 Processors 506 memory 508 Storage Devices 510 Input / Output Interfaces 512 Network Interfaces

Claims

1. It comprises a preprocessing unit, an acquisition unit, a identification unit, and an output unit. The aforementioned pre-processing unit, For each of the multiple images, generate at least one image description that describes its visual features. The image description is stored in association with each of the multiple images, The acquisition unit acquires input information including a linguistic representation of the visual features of the image. The identification unit matches the corresponding image description based on the input information and identifies the image associated with the image description. The output unit generates and outputs the response content for the image. Image search system.

2. The aforementioned pre-processing unit, Extract image attributes relating to the visual features from each of the multiple images, The image description text is generated, which includes the aforementioned image attributes in its content. The image attributes are further stored in association with each of the multiple images, The system further includes an attribute extraction unit that extracts search attributes related to the visual features from the input information, The specified unit further matches the corresponding image description based on the search attributes. The image search system according to claim 1.

3. If the aforementioned image is an icon, sign, symbol, or mark The input information includes a question relating to the icon, sign, symbol, or mark. The attribute extraction unit extracts the search attributes from the question, The identifying unit identifies the image attribute that matches the search attribute, and identifies the image associated with the image description containing the identified image attribute. The image search system according to claim 2, wherein the output unit generates an answer to the question as the answer content.

4. The preprocessing unit generates a prompt for image generation based on the image having the image attributes, The specified part, A search image is generated based on the prompt corresponding to the search attribute. Based on the searched image, a corresponding image is identified. The image search system according to claim 2.

5. Computers For each of the multiple images, generate at least one image description that describes its visual features. The image description is stored in association with each of the multiple images, Input information including a linguistic expression of the visual features of the aforementioned image is obtained. Based on the input information, the corresponding image description is matched, and the image associated with the image description is identified. The system generates and outputs the response content for the aforementioned image. How to search for images.

Images