WO2020026017A1 - Procédé et appareil de recommandation d'objet à base d'image, et dispositif/terminal/serveur - Google Patents

Procédé et appareil de recommandation d'objet à base d'image, et dispositif/terminal/serveur Download PDF

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Publication number
WO2020026017A1
WO2020026017A1 PCT/IB2018/056503 IB2018056503W WO2020026017A1 WO 2020026017 A1 WO2020026017 A1 WO 2020026017A1 IB 2018056503 W IB2018056503 W IB 2018056503W WO 2020026017 A1 WO2020026017 A1 WO 2020026017A1
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Prior art keywords
image
image data
information
feature information
shooting
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PCT/IB2018/056503
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English (en)
Chinese (zh)
Inventor
黄龙飞
蔡山清
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优视科技新加坡有限公司
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Publication of WO2020026017A1 publication Critical patent/WO2020026017A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes

Definitions

  • Image-based object recommendation method, device and device / terminal / server This application requires that the China Patent Office be filed on July 31, 2018, with application number 201810858651.2, and the invention name is "A method and device for image-based object recommendation And device / terminal / server "priority, the entire contents of which are incorporated in this application by reference.
  • the present application relates to the field of computer technology, and in particular, to an image-based object recommendation method, apparatus, and device / terminal / server.
  • the collection of user characteristics is based on user behavior, that is, access to websites, videos played, etc.
  • user behavior that is, access to websites, videos played, etc.
  • image shooting and image downloading only stay in the aspect of image sharing.
  • images captured or downloaded by users often have certain user characteristic information such as user preference information, and the prior art cannot push according to this user characteristic information.
  • Embodiments of the present application provide an image-based object recommendation method, device, and device / terminal / server, which implement object push based on image data by analyzing image data obtained.
  • an image-based object recommendation method includes: analyzing the obtained image data to obtain image feature information corresponding to the image data; and using the image feature information Performing comprehensive processing to obtain N-dimensional user characteristic information, where N is a natural number; and performing a similar object push processing operation on similar users according to the N-dimensional user characteristic information.
  • an image-based object recommendation device the device includes: an image analysis module configured to be based on the obtained image data Performing analysis to obtain image feature information corresponding to the image data; an information obtaining module configured to comprehensively process the image feature information to obtain N-dimensional user feature information, where N is a natural number; a push processing module, configured for Based on the N-dimension user characteristic information, a similar object is pushed for a similar object.
  • a device / terminal / server including: one or more processors; a storage device, configured to store one or more programs, and when the one or more programs are Being executed by the one or more processors, so that the one or more processors implement operations corresponding to the image-based object recommendation method as described above.
  • a computer-readable storage medium in which a computer program is stored, and when the program is executed by a processor, implements operations corresponding to the image-based object recommendation method described above .
  • the embodiment of the present application obtains the corresponding image feature information by analyzing the image data, and comprehensively processes the image feature information to obtain the N-dimensional user feature information and the N-dimensional user feature information.
  • Push similar objects The embodiments of the present application can thus collect user characteristic information through image data, and then push similar objects. This makes up for the shortcomings of image data that can only be used for sharing, achieves more accurate user pushes, and improves the accuracy and comprehensiveness of user pushes. Sex.
  • FIG. 1 is a flowchart of the steps of an image-based object recommendation method according to the first embodiment of the present application
  • FIG. 2 is a flowchart of steps of an implementation of step S101 of an image-based object recommendation method according to the first embodiment of the present application;
  • FIG. 3 is a flowchart of steps of another implementation of step S101 of an image-based object recommendation method according to the first embodiment of the present application;
  • FIG. 4 is a flowchart of steps in an implementation of step S103 of an image-based object recommendation method according to Embodiment 2 of the present application;
  • FIG. 5 is a structural block diagram of an image-based object recommendation device according to Embodiment 3 of the present application.
  • FIG. 6 is a structural block diagram of an implementation of an image analysis module of an image-based object recommendation device according to Embodiment 3 of the present application;
  • FIG. 7 is an image of an image-based object recommendation device according to Embodiment 3 of the present application Structural block diagram of another implementation of the analysis module;
  • FIG. 8 is a structural block diagram of an implementation of a push processing module of an image-based object recommendation device according to Embodiment 4 of the present application;
  • FIG. 9 is a structural block diagram of a device / terminal / server according to Embodiment 5 of the present application. detailed description
  • FIG. 1 a flowchart of the steps of an image-based object recommendation method according to the first embodiment of the present application is shown.
  • steps S 101 to S 103 described in this application do not represent the order of execution.
  • Step S101 Analyze the obtained image data to obtain image feature information corresponding to the image data.
  • the image data obtained in the embodiment of the present application includes image data captured by a user using a smart terminal, or image data downloaded by a user from other shooting devices and the Internet.
  • the embodiment of the present application performs analysis processing according to the obtained image data, including image analysis processing and image description file processing, so as to obtain image feature information corresponding to the image data.
  • the steps S 101 includes:
  • S 101 Analyze image feature data of the image data to obtain shooting feature information corresponding to the image data.
  • the image feature data of the image data may come from analysis processing of the image itself, or from analysis processing of a description file of the image.
  • the shooting feature information includes at least one of the following: the shooting scene of the image (night scene, daytime scene, etc.); the chroma, brightness, saturation, etc. of the image; the shooting tool of the image (mobile phone model, camera model, shooting APP, etc.) .
  • the step S101 includes:
  • the content characteristic information includes at least one of the following; a photographic subject (person, food, newspaper, movie ticket, painting, etc.); a shooting occasion (a downtown area, an amusement park, an exhibition hall, a restaurant, etc.).
  • the embodiments of the present application can obtain content feature information of image data according to the image feature data, so as to obtain user feature information representing user preferences according to the content feature information.
  • the step S 101 includes steps S 101 1 and S 1012.
  • Step S102 Comprehensively process the image feature information to obtain N-dimensional user feature information, where N is a natural number.
  • the N-dimensional user feature information includes:
  • At least one of shooting scene information, shooting image information, shooting tool information, and shooting content information At least one of shooting scene information, shooting image information, shooting tool information, and shooting content information.
  • the image feature information obtained by analyzing the image data is: an amusement park in a daytime scene
  • the image capturing tool is a Lycra camera
  • the image capturing content includes people and food.
  • the image feature information is comprehensively processed, and the N-dimensional user feature information is obtained as follows: a daytime playground is played, and a Leica camera is used to take a picture.
  • analysis based on image data to obtain image feature information is: a shopping mall in a daytime scene, the image capture tool is an Apple phone, and the image capture content includes people and food.
  • the image characteristic information is comprehensively processed, and the N-dimensional user characteristic information is obtained as follows: shopping in a mall in the daytime, and taking photos using an Apple mobile phone.
  • Step S103 Perform similar objects on similar users according to the N-dimensional user characteristic information Push processing operations.
  • a similar object corresponding to the N-dimensional user characteristic information is obtained and pushed to a corresponding user.
  • the N-dimensional user characteristic information is: playing in a playground in the daytime, and taking a picture using a Leica camera.
  • the embodiment of the present application pushes information on dining, accommodation, and traffic around the playground to corresponding users, and the embodiment of the present application can also push the information about the Leica camera peripheral products, shooting skills, and play routes of the playground to the user.
  • the N-dimensional user characteristic information is: shopping in a daytime mall, and using an Apple phone to take a photo.
  • This embodiment of the present application pushes information about catering, promotions, and traffic around the mall to corresponding users, and this embodiment of the present application can also push users with information about Apple's peripheral products and shooting techniques.
  • the embodiment of the present application obtains the corresponding image feature information by analyzing the image data, and comprehensively processes the image feature information to obtain N-dimensional user feature information, and pushes similar objects according to the N-dimensional user feature information.
  • the embodiments of the present application can thus collect user characteristic information through image data, and then push similar objects. This makes up for the shortcomings of image data that can only be used for sharing, achieves more accurate user pushes, and improves the accuracy and comprehensiveness of user pushes. Sex.
  • the image-based object recommendation method of this embodiment may be executed by any appropriate device having image-based object recommendation capabilities, including but not limited to: various device terminals or servers, including but not limited to PC, tablet, mobile Terminal, etc.
  • step S103 includes:
  • the collaborative clustering described in the embodiment of the present application includes at least one of the following: According to the user characteristic information, users having the same or similar user characteristic information are combined and classified into similar users. According to the user characteristic information, the objects satisfying the user characteristic information are combined and classified into similar objects. According to the user characteristic information, the corresponding relationships between users and objects are combined and classified into the same recommendation model. S1032. Obtain various types of object information corresponding to various types of users, and perform common push processing operations on the same types of users.
  • the embodiment of the present application may recommend similar objects to similar users according to the result of the collaborative clustering process.
  • the image-based object recommendation method of this embodiment may be executed by any appropriate device having image-based object recommendation capabilities, including but not limited to: various device terminals or servers, including but not limited to PC, tablet, mobile Terminal, etc.
  • FIG. 5 a structural diagram of an image-based object recommendation device according to Embodiment 3 of the present application is shown.
  • the image analysis module 501 is configured to perform analysis according to the obtained image data to obtain image feature information corresponding to the image data.
  • the information obtaining module 502 is configured to comprehensively process the image feature information to obtain N-dimensional user feature information, where N is a natural number.
  • the push processing module 503 is configured to perform a push processing operation on a similar object to a similar user according to the N-dimensional user characteristic information.
  • the image data obtained in the embodiment of the present application includes image data captured by a user using a smart terminal, or image data downloaded by a user from other shooting devices and the Internet.
  • the embodiment of the present application performs analysis processing according to the obtained image data, including image analysis processing and image description file processing, so as to obtain image feature information corresponding to the image data.
  • the image analysis module 501 includes: an image analysis unit 5011 configured to analyze image feature data of the image data to obtain shooting feature information corresponding to the image data.
  • the image feature data of the image data may come from analysis processing of the image itself, or from analysis processing of a description file of the image.
  • the shooting feature information includes at least one of the following: the shooting scene of the image (night scene, daytime scene, etc.); the chroma, brightness, saturation, etc. of the image; the shooting tool of the image (mobile phone model, camera model, shooting APP, etc.) .
  • the image analysis module 501 includes: a content analysis unit 5012 configured to analyze content feature data of the image data to obtain content feature information corresponding to the image data.
  • the content characteristic information includes at least one of the following; a photographic subject (person, food, newspaper, movie ticket, painting, etc.); a shooting occasion (a downtown area, an amusement park, an exhibition hall, a restaurant, etc.).
  • the embodiments of the present application can obtain content feature information of image data according to the image feature data, so as to obtain user feature information representing user preferences according to the content feature information.
  • the image analysis module 501 includes an image analysis unit 5011 and a content analysis unit 5012.
  • the N-dimensional user feature information includes:
  • At least one of shooting scene information, shooting image information, shooting tool information, and shooting content information At least one of shooting scene information, shooting image information, shooting tool information, and shooting content information.
  • the image feature information obtained by analyzing the image data is: an amusement park in a daytime scene
  • the image capturing tool is a Lycra camera
  • the image capturing content includes people and food.
  • the image feature information is comprehensively processed, and the N-dimensional user feature information is obtained as follows: a daytime playground is played, and a Leica camera is used to take a picture.
  • the analysis based on the image data to obtain image feature information is: a shopping mall in a daytime scene
  • the image capture tool is an Apple phone
  • the image capture content includes people and food.
  • the image feature information is comprehensively processed
  • the N-dimensional user feature information is obtained as follows: shopping in a daytime mall, and taking photos using an Apple phone.
  • a similar object corresponding to the N-dimensional user characteristic information is obtained and pushed to a corresponding user.
  • the N-dimensional user characteristic information is: playing in a playground in the daytime, and taking a picture using a Leica camera.
  • the embodiment of the present application pushes information on dining, accommodation, and traffic around the playground to corresponding users, and the embodiment of the present application can also push the information about the Leica camera peripheral products, shooting skills, and play routes of the playground to the user.
  • the N-dimensional user characteristic information is: shopping in a daytime mall, and using an Apple phone to take a photo.
  • This embodiment of the present application pushes information about catering, promotions, and traffic around the mall to corresponding users, and this embodiment of the present application can also push users with information about Apple's peripheral products and shooting techniques.
  • the embodiment of the present application obtains the corresponding image feature information by analyzing the image data, and comprehensively processes the image feature information to obtain N-dimensional user feature information, and pushes similar objects according to the N-dimensional user feature information.
  • the embodiments of the present application can thus collect user characteristic information through image data, and then push similar objects. This makes up for the shortcomings of image data that can only be used for sharing, achieves more accurate user pushes, and improves the accuracy and comprehensiveness of user pushes. Sex.
  • the image-based object recommendation method of this embodiment may be executed by any appropriate device having image-based object recommendation capabilities, including but not limited to: various device terminals or servers, including but not limited to PC, tablet, mobile Terminal, etc.
  • This embodiment includes the above-mentioned image analysis module 501, an information obtaining module 502, and a push processing module 503.
  • the push processing module 503 includes:
  • the collaborative clustering unit 5031 is configured to perform N-dimensional collaborative clustering processing on the same type of users according to the N-dimensional user characteristic information.
  • the common push unit 5032 is configured to obtain various types of object information corresponding to various types of users, and perform a common push processing operation on the similar objects to the similar users.
  • the collaborative clustering described in the embodiment of the present application includes at least one of the following: According to the user characteristic information, users having the same or similar user characteristic information are combined and classified into the same type. User. According to the user characteristic information, the objects satisfying the user characteristic information are combined and classified into similar objects. According to the user characteristic information, the corresponding relationships between users and objects are combined and classified into the same recommendation model.
  • the embodiment of the present application may recommend similar objects to similar users according to the result of the collaborative clustering process.
  • the embodiment of the present application obtains the corresponding image feature information by analyzing the image data, and comprehensively processes the image feature information to obtain N-dimensional user feature information, and pushes similar objects according to the N-dimensional user feature information.
  • the embodiments of the present application can thus collect user characteristic information through image data, and then push similar objects. This makes up for the shortcomings of image data that can only be used for sharing, achieves more accurate user pushes, and improves the accuracy and comprehensiveness of user pushes. Sex.
  • the image-based object recommendation device of this embodiment may be executed by any appropriate device having image-based object recommendation capabilities, including but not limited to: various device terminals or servers, including but not limited to PCs, tablets, and mobiles Terminal, etc.
  • FIG. 9 a structural block diagram of a device / terminal / server according to Embodiment 5 of the present application is shown.
  • the specific embodiment of the present application does not limit the specific implementation of the device / terminal / server.
  • the device / terminal / server may include: one or more processors
  • processor processing
  • memory storage device
  • the processor 902 is configured to execute a program 906, and may specifically perform relevant steps in the foregoing embodiment of the image-based object recommendation method.
  • the program 906 may include program code, where the program code includes a computer operation instruction.
  • the processor 902 may be a central processing unit CPU, or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.
  • One or more processors included in the device / terminal / server which can be processors of the same type, such as one or more CPUs, or different types Processors, such as one or more CPUs and one or more ASICs.
  • the storage device 904 is configured to store one or more programs 906.
  • the storage device 904 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
  • the program 906 may be specifically configured to cause the processor 902 to perform the following operations: analyze the obtained image data to obtain image feature information corresponding to the image data; perform comprehensive processing on the image feature information to obtain N-dimensional user feature information, The N is a natural number; and a similar object is pushed to a similar user according to the N-dimensional user characteristic information.
  • the program 906 is further configured to analyze image feature data of the image data to obtain shooting feature information corresponding to the image data.
  • the program 906 is further configured to analyze content feature data of the image data to obtain content feature information corresponding to the image data.
  • the N-dimensional user characteristic information includes:
  • At least one of shooting scene information, shooting image information, shooting tool information, and shooting content information At least one of shooting scene information, shooting image information, shooting tool information, and shooting content information.
  • the program 906 is further configured to perform N-dimensional collaborative clustering processing on similar users according to the N-dimensional user characteristic information; obtain various types of object information corresponding to various types of users, and Users perform common push processing operations on similar objects.
  • the embodiment of the present application obtains the corresponding image feature information by analyzing the image data, and comprehensively processes the image feature information to obtain N-dimensional user feature information, and pushes similar objects according to the N-dimensional user feature information.
  • the embodiments of the present application can thus collect user characteristic information through image data, and then push similar objects. This makes up for the shortcomings of image data that can only be used for sharing, achieves more accurate user pushes, and improves the accuracy and comprehensiveness of user pushes. Sex.
  • each component / step described in the embodiment of the present application may be split into more components / steps, or two or more components / steps or partial operations of components / steps may be combined into New components / steps to achieve the purpose of the embodiments of the present application.
  • the process described above with reference to the flowchart may be implemented as a computer software program.
  • embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable medium, the computer program containing program code for performing a method shown in a flowchart.
  • the computer The program may be downloaded and installed from a network through a communication section, and / or installed from a removable medium.
  • CPU central processing unit
  • the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the foregoing.
  • the computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable memories Programming read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • a computer-readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal that is included in baseband or propagated as part of a carrier wave, and which carries computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable medium may send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device .
  • Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Computer program code for performing the operations of the present application may be written in one or more programming languages or a combination thereof, the programming languages including an object-oriented programming language such as Java, Smalltalk, C ++, and also conventional A procedural programming language such as "C" or a similar programming language.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through the Internet using an Internet service provider) Connection).
  • LAN local area network
  • WAN wide area network
  • Internet service provider Internet service provider
  • each block in the flowchart or block diagram may represent a module, a program segment, or a portion of a code, which module, program segment, or part of the code contains one or more functions for implementing a specified logical function Executable instructions.
  • the functions marked in the blocks may also occur in a different order than those marked in the drawings. For example, two successively represented boxes may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts may be implemented in a dedicated hardware-based system that performs the specified function or operation. Or, it can be implemented by a combination of dedicated hardware and computer instructions.
  • a processor includes a receiving unit, a parsing unit, an information selecting unit, and a generating unit. Among them, the names of these units do not in any way constitute a limitation on the unit itself.
  • the receiving unit may also be described as a "unit for receiving a user's web browsing request".
  • the present application also provides a computer-readable storage medium having stored thereon a computer program, which is executed by a processor to implement a method as described in any one of the foregoing embodiments.
  • the present application further provides a computer-readable medium, which may be included in the device described in the foregoing embodiments; or may exist alone without being assembled into the device.
  • the computer-readable medium carries one or more programs, and when the one or more programs are executed by the device, the device is caused to: perform analysis according to the obtained image data to obtain image feature information corresponding to the image data;
  • the image feature information is comprehensively processed to obtain N-dimensional user feature information, where N is a natural number; and a similar user is pushed and operated on a similar object according to the N-dimensional user feature information.

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Abstract

Les modes de réalisation de la présente invention concernent un procédé et un appareil de recommandation d'objet à base d'image, ainsi qu'un dispositif/terminal/serveur. Le procédé comprend les étapes consistant à : effectuer une analyse en fonction de données données d'image acquises pour obtenir des informations de caractéristique d'image correspondant aux données d'image; effectuer un traitement intégré sur les informations de caractéristique d'image pour obtenir des informations N-dimensionnelles de caractéristique d'utilisateur, N étant un nombre naturel; et effectuer une opération de traitement de pousser sur des objets du même type pour des utilisateurs du même type en fonction des informations N-dimensionnelles de caractéristique d'utilisateur. Les modes de réalisation de la présente invention permettent d'effectuer un pousser d'objet sur la base de données d'image par analyse de données d'image acquises.
PCT/IB2018/056503 2018-07-31 2018-08-27 Procédé et appareil de recommandation d'objet à base d'image, et dispositif/terminal/serveur WO2020026017A1 (fr)

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CN201810858651.2A CN109063119A (zh) 2018-07-31 2018-07-31 一种基于图像的对象推荐方法、装置和设备/终端/服务器
CN201810858651.2 2018-07-31

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Citations (4)

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Publication number Priority date Publication date Assignee Title
US20120020576A1 (en) * 2008-10-03 2012-01-26 Peter Thomas Fry Interactive image selection method
US20120259701A1 (en) * 2009-12-24 2012-10-11 Nikon Corporation Retrieval support system, retrieval support method and retrieval support program
CN106156347A (zh) * 2016-07-21 2016-11-23 北京奇虎科技有限公司 云相册分类展示方法、装置及服务器
CN107203573A (zh) * 2016-03-18 2017-09-26 百度在线网络技术（北京）有限公司 一种基于关注点信息的信息推送方法及装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120020576A1 (en) * 2008-10-03 2012-01-26 Peter Thomas Fry Interactive image selection method
US20120259701A1 (en) * 2009-12-24 2012-10-11 Nikon Corporation Retrieval support system, retrieval support method and retrieval support program
CN107203573A (zh) * 2016-03-18 2017-09-26 百度在线网络技术（北京）有限公司 一种基于关注点信息的信息推送方法及装置
CN106156347A (zh) * 2016-07-21 2016-11-23 北京奇虎科技有限公司 云相册分类展示方法、装置及服务器

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