Processing device

Abstract

A processing device (200) comprises: an image data acquisition unit (221) that acquires image data of a target product; a product name acquisition unit (222) that acquires a product name of the product; a search unit (223) that executes a search with the product name acquired by the product name acquisition unit (222) as a search key to acquire pieces of image data from a Web site; a matching unit (224) that executes a matching process for searching the pieces of image data acquired by the search unit (223) for image data that can be determined to be the same as the image data acquired by the image data acquisition unit (221); and a processing unit (225) that executes a process according to the result of the matching process by the matching unit (224). With such a configuration, the processing device (200) can assist a user in making decisions.

Description

Processing equipment

[0001] The present disclosure relates to a processing device, a processing method, and a recording medium.

[0002] Product recognition processing may be performed using image data for the purpose of product confirmation, etc.

[0003] For example, Patent Document 1 is a document that describes such processing. Patent Document 1 describes a commodity recognition device that performs recognition processing based on image data (images). According to Patent Document 1, the commodity recognition device detects appearance features of a commodity contained in image data captured by an imaging unit from the image data. The commodity recognition device then compares the appearance feature data with feature data in a recognition dictionary file to extract commodity candidates contained in the image data. Furthermore, the commodity recognition device recognizes character strings contained in the image data captured by the imaging unit. Thereafter, the commodity recognition device determines the commodity to be recognized from the extracted commodity candidates based on the recognized character strings.

[0004] JP 2014-49015 A

[0005] In the case of the technology described in Patent Document 1, it is necessary to store image data or feature quantities of the recognition target in a storage device in advance. However, it is difficult to store image data of various recognition targets in a storage device in advance. As a result, there is a problem that information may not be stored in the storage device, and the recognition target may not be properly recognized.

[0006] Therefore, one of the objects of the present disclosure is to provide a processing device, a processing method, and a recording medium that can solve the above-mentioned problems.

[0007] In order to achieve this object, the processing device of the present disclosure has an image data acquisition unit that acquires image data of a target product; a product name acquisition unit that acquires the product name of the product; a search unit that acquires image data from a Web (World Wide Web) site by performing a search using the product name acquired by the product name acquisition unit as a search key; a matching unit that performs a matching process that searches the image data acquired by the search unit for image data that can be determined to be identical to the image data acquired by the image data acquisition unit; and a processing unit that performs processing according to the result of the matching process by the matching unit.

[0008] Furthermore, the processing method of the present disclosure is configured such that an information processing device acquires image data of a target product, acquires the product name of the product, acquires image data from a Web (World Wide Web) site by performing a search using the acquired product name as a search key, performs a matching process to search the image data acquired by the search for image data that can be determined to be identical to the image data of the target product, and performs processing according to the results of the matching process.

[0009] Furthermore, the recording medium in the present disclosure is a computer-readable recording medium having recorded thereon a program for causing an information processing device to perform the following processes: acquire image data of a target product, acquire the product name of the product, acquire image data from a Web (World Wide Web) site by performing a search using the acquired product name as a search key, perform a matching process to search the image data acquired by the search for image data that can be determined to be identical to the image data of the target product, and perform processing according to the results of the matching process.

[0010] According to the above-described configurations, it is possible to reduce the risk that the recognition target will not be properly recognized.

[0011] FIG. 1 is a diagram for explaining an overview of processing performed by a recognition device in the present disclosure. FIG. 2 is a block diagram showing an example of the configuration of a recognition device according to the present disclosure. FIG. 3 is a diagram for explaining an example of processing by a database matching unit. FIG. 4 is a diagram for explaining an example of product name acquisition processing. FIG. 5 is a diagram for explaining an example of acquired image matching processing. FIG. 6 is a diagram for explaining an example of processing by a processing unit. FIG. 7 is a diagram for explaining an example of processing by a processing unit. FIG. 8 is a diagram for explaining an example of processing by a processing unit. FIG. 9 is a flowchart showing an example of operation of a recognition device. FIG. 10 is a diagram showing an example of the hardware configuration of a processing device according to the present disclosure. FIG. 11 is a block diagram showing an example of the configuration of a processing device. FIG. 12 is a flowchart showing an example of operation of a processing device.

[0012] [First Embodiment] An example configuration of a recognition device 100 according to the present disclosure will be described with reference to Figs. 1 to 11. Fig. 1 is a diagram for explaining an overview of processing performed by the recognition device 100. Fig. 2 is a block diagram showing an example configuration of the recognition device 100. Fig. 3 is a diagram for explaining an example processing of the database matching unit 152. Fig. 4 is a diagram for explaining an example of product name acquisition processing. Fig. 5 is a diagram for explaining an example of acquired image matching processing. Figs. 6 to 10 are diagrams for explaining an example processing of the processing unit 157. Fig. 11 is a flowchart showing an example operation of the recognition device 100. Note that in the present disclosure, the drawings may be associated with one or more embodiments.

[0013] In a first embodiment of the present disclosure, as shown in FIG. 1 , a recognition device 100 is described, which is a processing device that performs recognition processing using image data of a product to be recognized. Referring to FIG. 1 , the recognition device 100 matches image data of the product to be recognized with image data stored in a product image database 142. The recognition device 100 then checks whether image data that can be determined to be identical to the image data of the product to be recognized is stored in the product image database 142 (process 1 in FIG. 1 ). If it is determined that image data that can be determined to be identical to the image data of the product to be recognized is stored in the product image database 142, the recognition device 100 identifies attributes of the product to be recognized according to the matching results. In other words, if the matching is successful, the recognition device 100 can determine whether the product to be recognized is genuine, counterfeit, or a genuine product according to the matching results.

[0014] Furthermore, if a match is not found through the above-described processing, the recognition device 100 acquires information indicating the product name of the product to be recognized by, for example, performing OCR (Optical Character Recognition) processing on the image data of the product to be recognized. Then, the recognition device 100 acquires image data corresponding to the product name by, for example, performing crawling using the acquired information, and temporarily stores the image data in the temporary database 143 (Process 2 in FIG. 1 ). The recognition device 100 then matches the image data of the product to be recognized with the image data acquired by crawling and performs processing according to the matching results. For example, if the image data acquired by crawling contains image data that can be determined to be identical to the image data of the product to be recognized, the recognition device 100 can identify the product attributes, etc., according to the matching results. In this case, the recognition device 100 may register the image data acquired by crawling, etc., that can be determined to be identical to the image data of the product to be recognized, in the product image database 142. The recognition device 100 may be configured to change the content of the processing according to the matching results, depending on, for example, whether the source of the image data acquired by crawling is reliable. The recognition device 100 may be configured to match only image data acquired by crawling that can be evaluated as coming from a trustworthy source. The recognition device 100 may also crawl only sites that can be determined as trustworthy.

[0015] Furthermore, if the image data of the product to be recognized does not match the image data acquired by crawling, the recognition device 100 can register the image data of the product to be recognized in the unknown image database 144 (process 3 in FIG. 1). In this way, registering unknown image data in the unknown image database 144 can encourage confirmation by an expert.

[0016] The recognition device 100 described in the present disclosure can be used in healthcare and medical care, for example, to determine whether a drug that has been purchased or is about to be purchased is genuine. For example, recognition using the recognition device 100 can confirm in advance whether the drug is genuine, thereby assisting a user in making a decision on whether to purchase the drug. However, the recognition device 100 may be used in situations and for objects other than those exemplified above. For example, the recognition device 100 can be used during inspection when delivering ordered products, during product recognition using a point-of-sale (POS) system using image recognition, or during other object recognition. Furthermore, the object recognized by the recognition device 100 is not limited to drugs, and may be any product.

[0017] In the following description, the image data of a product may be an image of a primary package that comes into direct contact with the product or medicine, or an image of an outer container or outer wrapping that further packages the primary package for retail, etc. In other words, the image data of a product may be an image of a primary package, or may be an image of a secondary package such as a product package.

[0018] 2 shows an example of the configuration of a recognition device 100, which is an information processing device that performs recognition processing using image data. Referring to Fig. 2, the recognition device 100 has, as main components, for example, an operation input unit 110, a screen display unit 120, a communication I / F unit 130, a storage unit 140, and an arithmetic processing unit 150.

[0019] 2 illustrates an example in which the functions of the recognition device 100 are realized using one information processing device. However, at least some of the functions of the recognition device 100 may be realized using multiple information processing devices, for example, on the cloud. Furthermore, the recognition device 100 may not include some of the components illustrated above, such as not having the operation input unit 110 or the screen display unit 120, or may have a configuration other than those illustrated above.

[0020] The operation input unit 110 is made up of operation input devices such as a keyboard, a mouse, etc. The operation input unit 110 detects operations of the operator operating the recognition device 100 and outputs the operations to the calculation processing unit 150.

[0021] The screen display unit 120 is composed of a screen display device such as a liquid crystal display, an organic electroluminescence (EL) display, etc. The screen display unit 120 can display various information stored in the storage unit 140 on the screen in response to instructions from the arithmetic processing unit 150.

[0022] The communication I / F unit 130 includes a data communication circuit, etc. The communication I / F unit 130 performs data communication with an external device connected via a communication line.

[0023] The storage unit 140 is a storage device such as a hard disk or memory. The storage unit 140 stores processing information and programs 145 required for various processes in the arithmetic processing unit 150. The programs 145 are read into the arithmetic processing unit 150 and executed to realize various processing units. The programs 145 are read in advance from an external device or recording medium via a data input / output function such as the communication I / F unit 130, and are stored in the storage unit 140. Main information stored in the storage unit 140 includes, for example, reliability information 141, a product image database 142, a temporary database 143, and an unknown image database 144.

[0024] The reliability information 141 is information indicating the reliability of the image source. For example, the reliability information 141 associates specific information for identifying the image source, such as a uniform resource locator (URL), with reliability judgment information indicating whether the associated site is reliable. For example, the reliability information 141 may include reliability judgment information based on the results of a prior investigation into whether the products or image data posted on the site can be trusted as authentic. Each piece of information included in the reliability information 141 is previously acquired by, for example, receiving information input using the operation input unit 110 or receiving information from an external device via the communication I / F unit 130, and is stored in the storage unit 140.

[0025] The reliability information 141 may include, for example, reliability judgment information for each seller on an EC (electronic commerce) site or the like, or may include information indicating criteria for evaluating sellers according to their ratings on the EC site. The reliability judgment information is information indicating whether a related site or the like is reliable, and may be, for example, an arbitrary value according to the level of reliability. The reliability judgment information may simply indicate whether or not a site is reliable, or may be more specific information, such as reliability of authenticity according to the likelihood that the site is genuine or reliability of a fake according to the likelihood that the site is fake.

[0026] The product image database 142 contains image data of products. For example, the product image database 142 contains information that associates product image data with information indicating product attributes, such as whether the product is genuine. Each piece of information contained in the product image database 142 is acquired in advance by, for example, receiving information from an external device via the communication I / F unit 130, and is stored in the storage unit 140. The product image database 142 can be updated by the processing unit 157, which will be described later, or the like.

[0027] Here, the information indicating product attributes can indicate the authenticity of a product, such as whether the related product is genuine or counterfeit. The information indicating product attributes may include, in addition to or instead of the above-mentioned examples, information indicating the likelihood of authenticity, such as a value indicating the likelihood of authenticity, or any other information. For example, the information indicating product attributes may include information indicating whether the authenticity information has been verified by an expert, or information indicating the registration history, such as whether the product was registered via crawling. Furthermore, product image data may include image data of the product captured from the front, as well as image data of the product captured from any angle. As described above, the image data included in the product image database 142 may be image data of primary packaging, secondary packaging, or the like.

[0028] The product image database 142 may include information indicating product attributes, product image data, information indicating product names, and other arbitrary information. The product image database 142 may also include, together with or instead of the image data, feature amounts extracted from the image data using an arbitrary method.

[0029] Image data acquired by crawling performed by a crawling unit 154 (described later) is temporarily stored in the temporary database 143. The temporary database 143 can be updated by the crawling unit 154, the processing unit 157, etc.

[0030] The unknown image database 144 temporarily stores image data that cannot be identified as authentic or counterfeit. As described above, the image data stored in the unknown image database is subject to verification by an expert. The unknown image database 144 can be updated by the processing unit 157 or the like.

[0031] The arithmetic processing unit 150 has an arithmetic device such as a CPU (Central Processing Unit) and its peripheral circuits. The arithmetic processing unit 150 reads and executes a program 145 from the storage unit 140, thereby causing the hardware and the program 145 to cooperate with each other to realize various processing functions. Major processing units realized by the arithmetic processing unit 150 include, for example, an image data acquisition unit 151, a database matching unit 152, a product name acquisition unit 153, a crawling unit 154, a reliability evaluation unit 155, an acquired image matching unit 156, and a processing unit 157.

[0032] In addition, instead of the above-mentioned CPU, the arithmetic processing unit 150 may have a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating point number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination of these.

[0033] The image data acquisition unit 151 acquires image data of a product to be recognized. For example, the image data acquisition unit 151 can acquire image data of a product to be recognized from an external device such as a camera connected via the communication I / F unit 130. The image data acquisition unit 151 may acquire image data using any method other than the methods exemplified above.

[0034] The database matching unit 152 matches the image data acquired by the image data acquisition unit 151 with each image data stored in the product image database 142. In other words, the database matching unit 152 performs a matching process of searching the image data stored in the product image database 142 for image data that can be determined to be identical to the image data of the product to be recognized.

[0035] In the present disclosure, the matching method performed by the database matching unit 152 is not particularly limited. For example, the database matching unit 152 extracts features from the image data acquired by the image data acquisition unit 151 using any means, such as using a model that has been machine-learned in advance to extract features in response to input image data. Then, as shown in FIG. 3 , the database matching unit 152 performs matching by checking whether the extracted features are similar to the features of each image data stored in the product image database 142. In this case, the database matching unit 152 may check whether the extracted features are similar to each image data of genuine items stored in the product image database 142, and may also check whether the extracted features are similar to each image data of counterfeit items. For example, the database matching unit 152 may calculate the distance between the features and determine whether the calculated distance exceeds a predetermined value. Furthermore, the database matching unit 152 may determine that the image data of the item to be recognized and the image data stored in the product image database 142 are identical when the calculated distance is equal to or less than a predetermined value. The database matching unit 152 may perform matching using a method other than the above example, such as using a model that has been machine-learned in advance. Furthermore, the database matching unit 152 may perform the above matching after performing any pre-processing, such as performing frontal correction by performing keystone correction on the area of ​​the image data acquired by the image data acquisition unit 151 that corresponds to the product.

[0036] If, as a result of matching, it is determined that image data that can be determined to be identical to the image data of the product to be recognized is stored in the product image database 142, information according to the matching result can be output by the processing unit 157, which will be described later. For example, if the image data that can be determined to be identical to the image data of the product to be recognized is image data that has the attributes of a genuine product, the processing unit 157 can output information that indicates that the product to be recognized is genuine. Furthermore, if the image data that can be determined to be identical to the image data of the product to be recognized is image data that has the attributes of a fake, the processing unit 157 can output information that indicates that the product to be recognized is a fake.

[0037] The product name acquisition unit 153 acquires information indicating the product name of the product to be recognized. For example, when the database matching unit 152 does not find image data that can be determined to be identical to the image data of the product to be recognized and it is determined that image data that can be determined to be identical is not stored in the product image database 142, the product name acquisition unit 153 may acquire information indicating the product name of the product to be recognized.

[0038] As an example, as shown in FIG. 4 , the product name acquisition unit 153 can acquire information indicating the product name of a product to be recognized by performing OCR processing on image data of the product to be recognized. Furthermore, there are cases where multiple character strings can be extracted by OCR processing, such as "ABCD" and "xyz" as shown in FIG. 4 . In such cases, the product name acquisition unit 153 can identify and extract, as the product name, a character string that satisfies a predetermined condition, such as the character string that occupies the largest area in the image data from which the character string is extracted, from among the multiple character strings extracted by OCR processing. For example, in the example shown in FIG. 4 , the area of ​​the region corresponding to the character string "ABCD" in the image data is larger than the area of ​​the region corresponding to the character string "xyz." Therefore, the product name acquisition unit 153 can identify and extract the character string "ABCD" as the product name. Note that the product name acquisition unit 153 may identify the product name from the multiple character strings extracted by OCR processing based on factors other than the area, such as the position of the extracted character string on the product and the presence or absence of decoration. For example, the product name acquisition unit 153 may identify and extract the product name depending on whether the extracted character string is located in the center of the product, whether the character string is decorated, or a combination of the above conditions.

[0039] The product name acquisition unit 153 may acquire the product name using a method other than the OCR processing exemplified above. For example, the product name acquisition unit 153 may accept input of information indicating the product name in response to an operation by an operator on the operation input unit 110. The product name acquisition unit 153 may also acquire information indicating the product name from an external device connected via the communication I / F unit 130.

[0040] The crawling unit 154 acquires image data corresponding to the product name by performing crawling using the product name acquired by the product name acquisition unit 153. In other words, the crawling unit 154 functions as a search unit that acquires image data from one or more Web (World Wide Web) sites by performing a search using the product name as a search key. The crawling unit 154 also stores the acquired image data in the temporary database 143.

[0041] In addition, the crawling unit 154 may crawl only highly reliable sites indicated by the reliability information 141 as sites from which image data may be obtained, or may crawl any site, not limited to the sites indicated by the reliability information 141.

[0042] The reliability evaluation unit 155 evaluates the reliability of the image acquisition source when the crawling unit 154 acquires image data. For example, the reliability evaluation unit 155 can evaluate the reliability of the site from which the image is acquired by referring to the reliability information 141.

[0043] The reliability evaluation unit 155 may evaluate reliability for each site, or in the case of an e-commerce site, may evaluate reliability for each seller on the e-commerce site. For example, the reliability evaluation unit 155 may evaluate reliability by modifying the evaluation of the site indicated by the reliability information 141 according to the evaluation of the seller corresponding to the page from which the image data was obtained. Furthermore, the reliability evaluation unit 155 may be configured to evaluate reliability when the crawling unit 154 did not perform crawling using the reliability information 141.

[0044] 5, the acquired image matching unit 156 matches the image data of the product to be recognized acquired by the image data acquisition unit 151 with the image data in the temporary database 143. In other words, the acquired image matching unit 156 performs a matching process of searching the image data acquired by the crawling unit 154 for image data that can be determined to be the same as the image data of the product to be recognized.

[0045] The acquired image matching unit 156 may perform matching using a method similar to that of the above-described database matching unit 152. The acquired image matching unit 156 may also be configured to target, for matching, only image data whose source has been determined by the reliability evaluation unit 155 to be reliable, from among the image data included in the temporary database 143.

[0046] Note that the acquired image matching unit 156 may not only simply confirm whether or not they are identical, but may also determine that they are not identical but cannot be said to be different, for example, when the distance between the feature amounts is equal to or greater than a predetermined value but less than a threshold value. In other words, the acquired image matching unit 156 may be configured to determine that there is a slight difference that cannot be determined to be identical when the distance between the image data of the product to be recognized and the image data acquired by crawling is within a predetermined range.

[0047] The processing unit 157 performs processing according to the matching results obtained by the database matching unit 152, the acquired image matching unit 156, etc. For example, the processing unit 157 can perform processing to identify attributes such as the authenticity of a product according to the matching results.

[0048] For example, as shown in FIG. 6 , if the database matching unit 152 determines that image data that can be determined to be identical to the image data of the product to be recognized is stored in the product image database 142, the processing unit 157 can output information according to the matching result. As an example, if the image data that can be determined to be identical to the image data of the product to be recognized is image data that has the attribute of authenticity, the processing unit 157 outputs information that indicates that the product to be recognized is authentic. Furthermore, if the image data that can be determined to be identical to the image data of the product to be recognized is image data that has the attribute of a fake, the processing unit 157 outputs information that indicates that the product to be recognized is a fake. For example, the processing unit 157 may display the above-mentioned information on the screen display unit 120 or may transmit it to an external device via the communication I / F unit 130.

[0049] Furthermore, as shown in FIG. 7 , if the acquired image matching unit 156 determines that image data that can be determined to be identical to the image data of the product to be recognized is stored in the temporary database 143, the processing unit 157 can identify the attributes of the product based on the matching results. For example, the processing unit 157 can identify that the product to be recognized is genuine. The processing unit 157 may also perform processing based on the reliability of the source of the image data acquired by crawling. For example, the processing unit 157 may identify that the product to be recognized is genuine if the reliability evaluation unit 155 evaluates the source of the image data as reliable. The processing unit 157 may identify that the product to be recognized is genuine if the reliability judgment information determines that the reliability of the genuine product on the acquisition source site is high and that the image data is likely to be genuine. The processing unit 157 may identify that the product to be recognized is fake if the reliability judgment information determines that the reliability of the counterfeit product on the acquisition source site is high and that the image data is likely to be fake. The processing unit 157 may also output the identification results, or, as shown in FIG. 8 , update the product image database 142 based on the identification results. For example, if the processing unit 157 determines that the product to be recognized is genuine, the processing unit 157 may update the product image database 142 by considering the image data acquired by crawling to be genuine image data. In other words, the processing unit 157 can register the image data identified as genuine in the product image database 142. At this time, the processing unit 157 may also register information indicating the registration history in the product image database 142. Furthermore, if the processing unit 157 determines that the product to be recognized is fake, the processing unit 157 may update the product image database 142 by considering the image data acquired by crawling to be fake image data.

[0050] Furthermore, if no match is found between the image data of the product to be recognized and the image data acquired by crawling, the processing unit 157 can register the image data of the product to be recognized in the unknown image database 144, as shown in Fig. 9. Furthermore, the processing unit 157 can prompt the expert to check the image data registered in the unknown image database 144. For example, the processing unit 157 may request the expert to check by notifying a pre-registered contact point or the like of the request for checking.

[0051] As illustrated above, the processing unit 157 can perform processing according to the matching results obtained by the database matching unit 152, the acquired image matching unit 156, or the like. As described above, the acquired image matching unit 156 may determine that subtle differences exist. In such cases, the processing unit 157 may be configured to output image data that emphasizes the differences between the image data, as illustrated in FIG. 10 . While such subtle differences may indicate a fake, they may also indicate authenticity. Therefore, if subtle differences exist, more detailed confirmation, such as component analysis, may be required, and output may be performed that emphasizes the differences. The emphasis on differences may be achieved by any means, such as using a machine-learned model. When outputting images that emphasize differences, the processing unit 157 may store the image data in either the product image database 142 or the unknown image database 144, or may not store the image data in either database.

[0052] The above is an example of the configuration of the recognition device 100. Next, an example of the operation of the recognition device 100 will be described with reference to Fig. 11. In other words, a processing method performed by the recognition device 100, which is a processing device, will be described with reference to Fig. 11.

[0053] 11 is a flowchart showing an example of the operation of the recognition device 100. Referring to FIG. 11, if the database matching unit 152 determines that image data that can be determined to be identical is not stored in the product image database 142 (No in step S101), the product name acquisition unit 153 acquires the product name (step S102). For example, the product name acquisition unit 153 can acquire information indicating the product name of the product to be recognized by performing OCR processing on the image data of the product to be recognized.

[0054] The crawling unit 154 acquires image data corresponding to the product name by performing crawling using the product name acquired by the product name acquisition unit 153. The crawling unit 154 also stores the acquired image data in the temporary database 143 (step S103).

[0055] When the crawling unit 154 acquires image data, the reliability evaluation unit 155 evaluates the reliability of the image acquisition source (step S104). For example, the reliability evaluation unit 155 can evaluate the reliability of the site from which the image is acquired by referring to the reliability information 141.

[0056] The acquired image matching unit 156 matches the image data of the product acquired by the image data acquisition unit 151 with the image data in the temporary database 143 (step S105).

[0057] The processing unit 157 performs processing according to the matching result by the acquired image matching unit 156 (step S106). For example, if the acquired image matching unit 156 determines that image data that can be determined to be identical to the image data of the product to be recognized is stored in the temporary database 143, the processing unit 157 can identify the attributes of the product according to the matching result. For example, the processing unit 157 can identify that the product to be recognized is genuine. The processing unit 157 may perform the identification according to the evaluation by the reliability evaluation unit 155. Furthermore, if the acquired image matching unit 156 determines that image data that can be determined to be identical to the image data of the product to be recognized is not stored in the temporary database 143, the processing unit 157 can store the image data in the unknown image database 144.

[0058] Furthermore, if the database matching unit 152 determines that image data that can be determined to be identical is stored in the product image database 142 (step S101, Yes), the processing unit 157 can output information according to the matching result (step S107). For example, if the image data that can be determined to be identical to the image data of the product to be recognized is image data that has the attribute of a genuine product, the processing unit 157 outputs information that indicates that the product to be recognized is genuine. Furthermore, if the image data that can be determined to be identical to the image data of the product to be recognized is image data that has the attribute of a fake, the processing unit 157 outputs information that indicates that the product to be recognized is a fake.

[0059] The above is an example of the operation of the recognition device 100.

[0060] As described above, the recognition device 100 includes a product name acquisition unit 153, a crawling unit 154, an acquired image matching unit 156, and a processing unit 157. With this configuration, the acquired image matching unit 156 can match image data of a product to be recognized with image data acquired by the crawling unit 154 using the product name acquired by the product name acquisition unit 153. As a result, even if image data is not stored in advance in the product image database 142, the processing unit 157 can perform matching using the crawling results and identify attributes according to the matching results. As a result, even if the recognition target cannot be properly recognized due to reasons such as insufficient image data stored in the product image database 142, it is possible to perform recognition processing and identify the attributes of the product.

[0061] The recognition device 100 also includes a reliability evaluation unit 155. With this configuration, the processing unit 157 can perform processing in accordance with the evaluation of the image data acquisition source by the reliability evaluation unit 155. As a result, the recognition device 100 can perform recognition processing more appropriately.

[0062] Furthermore, the processing unit 157 can update the product image database 142 in accordance with the matching results by the acquired image matching unit 156. For example, if the acquired image matching unit 156 identifies the product to be recognized as genuine, the processing unit 157 can update the product image database 142 by assuming that the image data acquired by crawling is genuine image data. With this configuration, the recognition device 100 can automatically update the product image database 142 using the crawling results. This can reduce the effort required to update the product image database 142.

[0063] Second Embodiment Next, a processing device 200, which is an information processing device that performs processing according to the matching result, will be described with reference to Fig. 12 to Fig. 14. Fig. 12 is a diagram showing an example of the hardware configuration of the processing device 200. Fig. 13 is a block diagram showing an example of the configuration of the processing device 200. Fig. 14 is a flowchart showing an example of the operation of the processing device 200.

[0064] Fig. 12 shows an example of the hardware configuration of the processing device 200. Referring to Fig. 12, the processing device 200 has the following hardware configuration, as an example: CPU (Central Processing Unit) 201 (arithmetic device) ROM (Read Only Memory) 202 (storage device) RAM (Random Access Memory) 203 (storage device) Programs 204 loaded into RAM 203 Storage device 205 storing the programs 204 Drive device 206 for reading and writing from / to a storage medium 210 outside the information processing device Communication interface 207 for connecting to a communication network 211 outside the information processing device Input / output interface 208 for inputting and outputting data Bus 209 for connecting the respective components

[0065] 13 . The programs 204 may be stored in the storage device 205 or the ROM 202 in advance, and the CPU 201 may load the programs into the RAM 203 or the like and execute them as needed. The programs 204 may be supplied to the CPU 201 via the communication network 211, or may be stored in the recording medium 210 in advance, with the drive device 206 reading out the programs and supplying them to the CPU 201.

[0066] 12 shows an example of the hardware configuration of the processing device 200. The hardware configuration of the processing device 200 is not limited to the above-described case. For example, the processing device 200 may be configured with only a part of the above-described configuration, such as excluding the drive device 206. Furthermore, the CPU 201 may be a GPU, etc., as exemplified in the first embodiment.

[0067] The image data acquisition unit 221 acquires image data of the target product. For example, the image data acquisition unit 221 may acquire image data from an imaging device such as a camera that captures an image of the product.

[0068] The product name acquisition unit 222 acquires the product name of the target product. For example, the product name acquisition unit 222 can acquire the product name of the target product by performing OCR processing on the image data acquired by the image data acquisition unit 221.

[0069] The search unit 223 performs a search using the product name acquired by the product name acquisition unit 222 as a search key, thereby acquiring image data from the website.

[0070] The matching unit 224 performs a matching process to search the image data acquired by the search unit 223 for image data that can be determined to be the same as the image data acquired by the image data acquisition unit 221. The matching process may be performed using any method, such as performing the matching process according to the distance between feature amounts extracted from the image data.

[0071] The processing unit 225 performs processing according to the result of the matching process by the matching unit. For example, the processing unit 225 can perform processing to identify attributes such as the authenticity of the product according to the matching result.

[0072] The above is an example of the configuration of the processing device 200. Next, an example of the operation of the processing device 200 will be described with reference to FIG.

[0073] 14 shows an example of the operation of the processing device 200. Referring to Fig. 14, the image data acquisition unit 221 acquires image data of a target product (step S201). For example, the image data acquisition unit 221 may acquire image data from an imaging device such as a camera that captures an image of the product.

[0074] The product name acquisition unit 222 acquires the product name of the target product (step S202). For example, the product name acquisition unit 222 can acquire the product name of the target product by performing OCR processing on the image data acquired by the image data acquisition unit 221.

[0075] The search unit 223 performs a search using the product name acquired by the product name acquisition unit 222 as a search key to acquire image data from the website (step S203).

[0076] The matching unit 224 performs a matching process (step S204) to search the image data acquired by the search unit 223 for image data that can be determined to be the same as the image data acquired by the image data acquisition unit 221. The matching process may be performed using any method, such as by using a distance between feature amounts extracted from the image data.

[0077] The processing unit 225 performs processing according to the result of the matching process by the matching unit (step S205). For example, the processing unit 225 can perform processing to identify attributes such as the authenticity of the product according to the matching result.

[0078] As described above, the processing device 200 has a product name acquisition unit 222, a search unit 223, a matching unit 224, and a processing unit 225. With this configuration, the matching unit 224 can perform a matching process in which the product name acquired by the product name acquisition unit 222 is used to search for image data that meets certain conditions from among the image data acquired by the search unit 223. As a result, the processing unit 225 can perform processing according to the results of the matching process. This allows the processing to identify attributes such as the authenticity of the product according to the matching results.

[0079] The processing device 200 described above can be realized by incorporating a predetermined program into an information processing device such as the processing device 200. Specifically, a program according to another embodiment of the present disclosure is a program for realizing processing in an information processing device, which includes acquiring image data of a target product, acquiring the product name of the product, acquiring image data from a website by performing a search using the acquired product name as a search key, performing a matching process to search the image data acquired by the search for image data that can be determined to be identical to the image data of the target product, and performing processing according to the result of the matching process.

[0080] Furthermore, the processing method executed by an information processing device such as the processing device 200 described above is a method in which the information processing device acquires image data of the target product, acquires the product name of the product, acquires image data from a website by performing a search using the acquired product name as a search key, performs a matching process to search the image data acquired by the search for image data that can be determined to be identical to the image data of the target product, and performs processing according to the results of the matching process.

[0081] Any program having the above-described configuration, or a computer-readable recording medium having the program recorded thereon, or a processing method, etc., can achieve the same functions and effects as the above-described processing device 200, and therefore can achieve the above-described objective of the present disclosure.

[0082] <Supplementary Notes> Part or all of the above-described embodiments can also be described as follows: The determining device and other components of the present disclosure will be outlined below. However, the present disclosure is not limited to the following configuration.

[0083] (Supplementary Note 1) A processing device comprising: an image data acquisition unit that acquires image data of a target product; a product name acquisition unit that acquires the product name of the product; a search unit that acquires image data from a Web (World Wide Web) site by performing a search using the product name acquired by the product name acquisition unit as a search key; a matching unit that performs a matching process to search the image data acquired by the search unit for image data that can be determined to be identical to the image data acquired by the image data acquisition unit; and a processing unit that performs processing in accordance with the result of the matching process by the matching unit. (Supplementary Note 2) The processing device according to Supplementary Note 1, further comprising: a reliability evaluation unit that evaluates whether a source of the image data acquired by the search unit is reliable using information stored in advance, and the processing unit performs processing in accordance with the reliability of the image data source evaluated by the reliability evaluation unit. (Supplementary Note 3) The processing device according to Supplementary Note 2, wherein the processing unit identifies the target product as genuine if the reliability evaluation unit evaluates the source of the image data to be reliable. (Supplementary Note 4) The processing device according to Supplementary Note 2 or Supplementary Note 3, wherein the matching unit searches for image data that can be determined to be identical to the image data acquired by the image data acquisition unit from image data whose acquisition source has been evaluated by the reliability evaluation unit as being reliable, among the image data acquired by the search unit. (Supplementary Note 5) The processing device according to any one of Supplementary Note 2 to Supplementary Note 4, wherein the search unit acquires image data from a website that can be evaluated as being reliable based on pre-stored information. (Supplementary Note 6) The processing device according to any one of Supplementary Note 1 to Supplementary Note 5, wherein the processing unit registers image data that can be determined to be identical to the image data acquired by the image data acquisition unit, among the image data acquired by the search unit, in an image database that registers image data of products.(Supplementary Note 7) The processing device according to any one of Supplementary Notes 1 to 6, wherein the product name acquisition unit acquires the product name by performing OCR (Optical Character Recognition) processing on the image data acquired by the image data acquisition unit. (Supplementary Note 8) The processing device according to Supplementary Note 7, wherein the product name acquisition unit extracts, from a plurality of character strings extracted by performing OCR processing, a character string that occupies the largest area in the image data from which the product name is extracted. (Supplementary Note 9) The processing device according to any one of Supplementary Notes 1 to 8, further comprising a database matching unit that performs a matching process to search image data that can be determined to be identical to the image data acquired by the image data acquisition unit from image data stored in advance, and the product name acquisition unit acquires the product name of the product when image data that can be determined to be identical to the image data acquired by the image data acquisition unit is not found from the image data stored in advance. (Supplementary Note 10) The processing device according to any one of Supplementary Notes 1 to 9, wherein the matching unit performs the matching process using a model that has been machine-learned in advance. (Supplementary Note 11) A processing method in which an information processing device acquires image data of a target product, acquires the product name of the product, acquires image data from a Web (World Wide Web) site by performing a search using the acquired product name as a search key, performs a matching process to search the image data acquired by the search for image data that can be determined to be identical to the image data of the target product, and performs processing according to the results of the matching process.(Supplementary Note 12) A computer-readable recording medium having recorded thereon a program for implementing the following processes in an information processing device: acquiring image data of a target product, acquiring the product name of the product, acquiring image data from a Web (World Wide Web) site by performing a search using the acquired product name as a search key, performing a matching process to search the image data acquired by the search for image data that can be determined to be identical to the image data of the target product, and performing processing according to the results of the matching process.

[0084] Note that some or all of the configurations described in Supplementary Notes 2 to 9 that are dependent on the processing device described in Supplementary Note 1 may also be dependent in a similar dependent relationship on the processing method described in Supplementary Note 10 and the recording medium described in Process 11. Furthermore, not limited to Supplementary Notes 10 and 11, some or all of the configurations described as Supplements may also be dependent on various hardware, software, various recording means for recording software, or systems within the scope of the above-described embodiments.

[0085] The programs described in the above embodiments and appendices may be stored in a storage device or a computer-readable recording medium, such as a portable medium such as a flexible disk, an optical disk, a magneto-optical disk, or a semiconductor memory.

[0086] Although the present disclosure has been described above with reference to the above-described embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0087] 100 Recognition device 110 Operation input unit 120 Screen display unit 130 Communication I / F unit 140 Storage unit 141 Reliability information 142 Product image database 143 Temporary database 144 Unknown image database 145 Program 150 Arithmetic processing unit 151 Image data acquisition unit 152 Database matching unit 153 Product name acquisition unit 154 Crawling unit 155 Reliability evaluation unit 156 Acquired image matching unit 157 Processing unit 200 Processing device 201 CPU 202 ROM 203 RAM 204 Program group 205 Storage device 206 Drive device 207 Communication interface 208 Input / output interface 209 Bus 210 Recording medium 211 Communication network 221 Image data acquisition unit 222 Product name acquisition unit 223 Search unit 224 Matching unit 225 Processing section

Claims

1. A processing device having: an image data acquisition unit that acquires image data of a target product; a product name acquisition unit that acquires the product name of the product; a search unit that acquires image data from a Web (World Wide Web) site by conducting a search using the product name acquired by the product name acquisition unit as a search key; a matching unit that performs a matching process to search among the image data acquired by the search unit for image data that can be determined to be identical to the image data acquired by the image data acquisition unit; and a processing unit that performs processing according to the result of the matching process by the matching unit.

2. A processing device according to claim 1, further comprising a reliability evaluation unit which uses pre-stored information to evaluate whether the source of the image data acquired by the search unit is reliable or not, and the processing unit performs processing according to the reliability of the image data source evaluated by the reliability evaluation unit.

3. A processing device according to claim 2, wherein the processing unit identifies the target product as genuine when the reliability evaluation unit evaluates the source of the image data as being reliable.

4. A processing device as described in claim 2, wherein the matching unit searches for image data that can be determined to be identical to the image data acquired by the image data acquisition unit from among the image data acquired by the search unit and whose source has been evaluated by the reliability evaluation unit as being reliable.

5. A processing device according to claim 2, wherein the search unit acquires image data from a website that can be evaluated as being reliable based on pre-stored information.

6. A processing device according to claim 1, wherein the processing unit registers image data acquired by the search unit that can be determined to be identical to image data acquired by the image data acquisition unit in an image database in which product image data is registered.

7. A processing device according to claim 1, wherein the product name acquisition unit acquires the product name of the product by performing OCR (Optical Character Recognition) processing on the image data acquired by the image data acquisition unit.

8. A processing device according to claim 7, wherein the product name acquisition unit extracts, from among a plurality of character strings extracted by performing OCR processing, a character string that occupies the largest area in the original image data as the product name.

9. A processing device as described in claim 1, comprising a database matching unit that performs a matching process to search for image data that can be determined to be identical to the image data acquired by the image data acquisition unit among pre-stored image data, and the product name acquisition unit acquires the product name of the product when image data that can be determined to be identical to the image data acquired by the image data acquisition unit is not found among the pre-stored image data.

10. A processing device according to claim 1, wherein the matching unit performs matching processing using a model that has been machine-learned in advance.

11. A processing method in which an information processing device acquires image data of a target product, acquires the product name of the product, acquires image data from a World Wide Web site by performing a search using the acquired product name as a search key, performs a matching process to search for image data that can be determined to be identical to the image data of the target product from among the image data acquired by the search, and performs processing according to the results of the matching process.

12. A computer-readable recording medium having recorded thereon a program for implementing the following processes in an information processing device: acquiring image data of a target product, acquiring the product name of said product, acquiring image data from a Web (World Wide Web) site by performing a search using the acquired product name as a search key, performing a matching process in which image data that can be determined to be identical to the image data of the target product is searched for among the image data acquired by the search, and performing processing according to the results of the matching process.

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