CN109492121A - Method, apparatus, server and the storage medium of intelligent recognition picture - Google Patents
Method, apparatus, server and the storage medium of intelligent recognition picture Download PDFInfo
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Abstract
A kind of method of intelligent recognition picture pays close attention to the operation of picture file the method includes receiving user, and establishes user according to the operation and pay close attention to list, wherein the concern list includes the picture file and its corresponding characteristic value；Obtain the Target Photo file that user currently browses；Calculate the characteristic value of Target Photo file；User is inquired according to the characteristic value of Target Photo file and pays close attention to list, to confirm whether Target Photo file has been concerned；And when the characteristic value of Target Photo file is present in user and pays close attention in list, confirmation Target Photo file has been concerned, and prompts user without paying close attention to the Target Photo file.The present invention also provides device, server and the storage mediums of a kind of intelligent recognition picture.The present invention can solve user it is excessive in the picture of concern and when can not identify the same picture of different user publication, the problem of repeatedly concern the same picture, can also be classified by classifier to picture file.
Description
Technical Field
The invention relates to the technical field of image recognition, in particular to a method, a device, a server and a storage medium for intelligently recognizing pictures.
Background
These photo browsing social software like Instgram, Tumblr, etc. basically require user registration to use. And in the using process, a user can pay attention to the browsed pictures according to the preference of the user (such as praise and collection). With the increase of the base number of the concerned pictures, the user may not be able to identify whether the same picture published by different users has been concerned, so that the user may pay attention to the same picture repeatedly.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a server and a storage medium for intelligently identifying pictures, which filter pictures by calculating feature values of the pictures, avoid repeated attention of the same picture, and improve user experience.
The first aspect of the present invention provides a method for intelligently identifying pictures, the method comprising:
receiving an operation of a user concerning an image file, and establishing a user concerning list according to the operation, wherein the concerning list comprises the image file and a characteristic value corresponding to the image file;
acquiring a target picture file currently browsed by a user;
calculating a characteristic value of the target picture file;
inquiring the user attention list according to the characteristic value of the target picture file to confirm whether the target picture file is paid attention; and
and when the characteristic value of the target picture file exists in the user attention list, confirming that the target picture file is paid attention to, and prompting the user not to pay attention to the target picture file.
Preferably, before the receiving an operation of the user concerning the picture file and establishing the user concerning list according to the operation, the method further includes:
receiving a picture file uploaded by electronic equipment;
and calculating the characteristic value of the picture file.
Preferably, after receiving a picture file uploaded by an electronic device, the method further includes a step of classifying the picture file, where the step includes:
preprocessing the received picture file;
extracting the characteristics of the preprocessed pictures; and
and classifying the pictures after the features are extracted through a classifier.
Preferably, the calculating the feature value of the picture file includes:
and calculating the characteristic values of the picture files by adopting different calculation methods according to the different types of the classified picture files.
Preferably, the method for classifying the image after feature extraction by the classifier includes an image classification method based on a generative model and an image classification method based on a discriminant model.
Preferably, the step of acquiring a target picture file currently browsed by a user includes:
the method comprises the steps that a web crawler captures web page content currently browsed by a user, wherein the web page content comprises a web page structure;
and acquiring a target picture file currently browsed by the user according to the webpage structure.
Preferably, when the feature value of the target picture file does not exist in the user attention list, it is determined that the target picture file is not paid attention, the user is prompted to pay attention to the target picture file, and the target picture file and the feature value thereof are added to the attention list.
A second aspect of the present invention provides an apparatus for intelligently recognizing pictures, the apparatus comprising:
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for receiving the operation of the user concerning the picture file and establishing a user concerning list according to the operation, and the concerning list comprises the picture file and a characteristic value corresponding to the picture file;
the acquisition module is used for acquiring a target picture file currently browsed by a user;
the calculation module is used for calculating the characteristic value of the target picture file;
the query module is used for querying the user attention list according to the characteristic value of the target picture file so as to confirm whether the target picture file is concerned; and
and the prompting module is used for confirming that the target picture file is concerned when the characteristic value of the target picture file exists in the user concerned list and prompting the user not to concern the target picture file.
A third aspect of the invention provides a server comprising a processor and a memory, the processor being configured to implement the method of intelligently recognizing pictures when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of intelligently recognizing pictures.
According to the method, the device, the server and the storage medium for intelligently identifying the picture, disclosed by the invention, the user attention list is established, wherein the attention list comprises the characteristic value, the picture file currently browsed by the user is obtained, and the characteristic value of the picture file is calculated; and comparing whether the characteristic value is consistent with the characteristic value in the attention list or not, and prompting a user not to pay attention to the picture file when the characteristic value is consistent with the characteristic value in the attention list. Therefore, the problem that a user pays attention to the same picture for many times when the concerned pictures are too many to identify the same picture issued by different users can be solved. The pictures can be filtered by calculating the characteristic values of the pictures, so that the repeated attention of the same picture is avoided, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is an application environment architecture diagram of a method for intelligently recognizing a picture according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for intelligently recognizing a picture according to a second embodiment of the present invention.
Fig. 3 is a functional block diagram of an apparatus for intelligently recognizing pictures according to a third embodiment of the present invention.
Fig. 4 is a schematic diagram of a server according to a fourth embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The terms "first," "second," and "third," etc. in the description and claims of the present invention and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "comprises" and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The method for intelligently identifying the picture is applied to a hardware environment formed by at least one server and electronic equipment connected with the server through a network. Networks include, but are not limited to: a wide area network, a metropolitan area network, or a local area network. The method for intelligently identifying the picture can be executed by the server or the electronic equipment; or may be performed by both the server and the electronic device.
The server which needs to carry out the method for intelligently identifying the picture can directly integrate the function of intelligently identifying the picture provided by the method on the server or install a client for realizing the method of the invention. For another example, the method provided by the present invention may also be run on a device such as a server in the form of a Software Development Kit (SDK), and an interface for intelligently identifying the picture function is provided in the form of an SDK, and the server or other devices may implement the function of intelligently identifying the picture through the provided interface.
Example one
Fig. 1 is a diagram illustrating an application environment architecture of a method for intelligently recognizing pictures according to an embodiment of the present invention.
The method for intelligently identifying the picture is applied to an environment consisting of the electronic equipment 1 and the server 2. The electronic device 1 and the server 2 are connected through wired or wireless network communication. The wired network may be any type of conventional wired communication, such as the internet, a local area network. The Wireless network may be of any type of conventional Wireless communication, such as radio, Wireless Fidelity (WIFI), cellular, satellite, broadcast, etc. The wireless communication technology may include, but is not limited to, Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), wideband Code Division multiple Access (W-CDMA), CDMA2000, IMT single carrier (IMT single carrier), Enhanced Data rate GSM Evolution (Enhanced Data Rates for GSM Evolution), Long Term Evolution (EDGE), Long Term Evolution (Long-Term Evolution, LTE), advanced Long Term Evolution (LTE), Time-Division Long Term Evolution (TD-LTE), fifth generation Mobile communication technology (5G), high performance Radio Local Area Network (high lan), high performance Radio Wide Area Network (high Area Network, Wide Area Network (wan), and Wide Area Network (Wide Area Network for Global Area Network (wan), WiMAX), ZigBee protocol (ZigBee), bluetooth, Orthogonal Frequency Division Multiplexing (Flash Orthogonal-Division Multiplexing, Flash-OFDM), High Capacity space Division Multiple Access (HC-SDMA), Universal Mobile Telecommunications System (UMTS), Universal Mobile Telecommunications System Time Division duplex (UMTS Time Division Multiplexing, UMTS-TDD), Evolved High speed Packet Access (Evolved High speed Packet Access, HSPA +), Time Division Synchronous code Division Multiple Access (TD-SCDMA), Evolved Data optimization (EV-Data), digitally Enhanced Cordless communication (Digital Enhanced Cordless Telecommunications, Digital and other Digital Enhanced Cordless Telecommunications).
The electronic device 1 may include a Personal Computer (PC), a Personal Digital Assistant (PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a smart phone, and the like. The electronic device 1 is merely an example, and is not exhaustive, and includes but is not limited to the electronic device 1. The movement may be human-computer interactive with a user through a keyboard, mouse, remote control, touch pad or voice control device, etc.
In this embodiment, an application program is installed on the electronic device 1, and a user can upload a picture file through the application program.
The application program may be any third-party application installed in the operating system of the electronic device 1, such as social software that can perform picture browsing, including WeChat, microblog, QQ, Instgram, Tumblr, American show, and the like. The present embodiment is not limited to this. The operating system includes an Android system, a saiban system, a Windows system, an ios (mobile operating system developed by apple inc.) system, and the like.
The electronic device 1 further comprises a Display screen, which may have a touch function, such as a Liquid Crystal Display (LCD) Display screen or an Organic Light-Emitting Diode (OLED) Display screen. The display screen is used for displaying information such as the picture file.
The server 2 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and hardware thereof includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable gate array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
Example two
Fig. 2 is a flowchart of a method for intelligently recognizing a picture according to a second embodiment of the present invention. The execution sequence in the flow chart can be changed and some steps can be omitted according to different requirements.
In this embodiment, the method for intelligently recognizing a picture may be applied to a server, and the functions provided by the method of the present invention for intelligently recognizing a picture may be directly integrated on the electronic device, or may be run on the server in a Software Development Kit (SDK) form.
And S1, receiving the picture file.
In this embodiment, a picture file uploaded by an electronic device is received. The electronic device may include, but is not limited to, a Personal Computer (PC), a Personal Digital Assistant (PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a smart phone, and the like. The above mentioned electronic devices are only examples, not exhaustive, and include but not limited to the above mentioned electronic devices.
In this embodiment, an application program is installed on the electronic device, and a user can upload a picture file through the application program.
The application program can be any third-party application installed in an operating system of the electronic device, such as social software capable of browsing pictures, including WeChat, microblog, QQ, Instgram, Tumblr, American show and the like. The present embodiment is not limited to this. The operating system includes an Android system, a saiban system, a Windows system, an ios (mobile operating system developed by apple inc.) system, and the like.
Preferably, after receiving the picture file, the method further comprises the step of classifying the received picture file. The categories of pictures may include, but are not limited to, landscape, people, animals, buildings, and the like.
It is understood that the present scheme can classify the types of the pictures in multiple levels. For example, the picture files are firstly classified into scenery, people, animals, buildings and the like; the image files after the primary classification are classified into a secondary classification, for example, animals are subdivided into cats, dogs, birds, fish, etc., and people are classified into men and women, or into elderly, middle-aged, teenagers, children, etc.
Specifically, the step of classifying the received picture file includes:
a) preprocessing the received picture file; the preprocessing mainly comprises image segmentation, image enhancement, morphological processing and the like.
b) Extracting the characteristics of the preprocessed pictures; the feature extraction is mainly divided into two types: the system comprises bottom layer visual features and intermediate semantic features, wherein the bottom layer visual features mainly comprise simple features such as colors, shapes and textures and complex local invariant features such as SIFT, RIFT and HOG; the middle semantic features mainly comprise semantic attribute features, regional semantic concept features and bag-of-words features.
c) And classifying the pictures after the features are extracted through a classifier. The classifier can help a user to establish an accurate mapping relation between image features and keyword categories and extract image semantic information consistent with user cognition.
Preferably, the method for classifying the image after feature extraction by the classifier includes an image classification method based on a generative model and an image classification method based on a discriminant model. The image classification method based on the generated model is established on the basis of the joint probability distribution of the image characteristics and the image categories, and the image classification method based on the discriminant model is established on the basis of the conditional probability distribution of the image characteristics and the image categories.
And S2, calculating the characteristic value of the picture file.
In this embodiment, after classifying the picture files, different calculation methods may be selected according to different types of the picture files to calculate the feature values of the picture files.
For example, the feature value may be a vector of a certain dimension, such as (P1, P2.., Pn), which is an n-dimensional vector that may be used to describe the shape feature of an object in an image. In the prior art, Hough (Hough) transform may be used to calculate the feature values. The Hough transform is a common algorithm and is not described herein.
In this embodiment, a preferred method for calculating a feature value of a picture file with a first class of a building is described, which may include the following steps:
a1: extracting the image of the contained building from the original image;
a2: filling the boundary of the extracted image with a single color as a background, and enabling the filled image to be a minimum square;
a3: scaling the whole image of the square image into an image with a first preset size in an equal ratio, and dividing the scaled image into sub image blocks with a second preset size;
a4: respectively calculating brightness derivatives of adjacent pixels in horizontal, vertical, positive 45 degrees and negative 45 degrees directions of the sub-image blocks, and taking the number of extreme points of the derivatives in the four directions and the total number of the extreme points on four boundaries of the sub-image blocks as feature vectors of the sub-image blocks;
a5: and taking the feature vectors of all the sub image blocks as the feature vectors of the original image.
The image feature extraction method mainly utilizes the brightness difference between the edge part of an object displayed in an image and the surrounding background and other pixel brightness to find the edge of the object, and can also extract the shape feature of the object in the image, so that the shape feature can be used as the feature value of the image.
In addition, the characteristic value of the picture file with the primary category of people can be calculated by adopting a singular value decomposition method. The method specifically comprises the following steps:
b1: setting A ∈ Rm×nA face gray image is obtained, wherein m is more than or equal to n, and rank (A) r;
b2: two orthogonal matrices and a diagonal matrix are obtained as follows:
U＝[u1,u2,...,um]∈Rm×m,UTU＝I,
the orthogonal matrix is: v ═ V1,v2,...,vn]∈Rn×n,VTV＝I
The diagonal matrix is: s ═ diag [ lambda ]1,λ2,...,λr,0,…,0]∈Rm×n,λ1＞λ2＞…＞λr≥0；
B3: so that the following formula is established,wherein,is ATA and AATCharacteristic value of (u)iAnd viAre each AATAnd ATA corresponds toThe feature vector of (2);
b4: the projection form of the above equation is obtained, i.e. the projection of the image a on U, V is a diagonal matrix S, S ═ UTAnd AV, a vector formed by elements on the diagonal line of S is the singular characteristic value of the image A.
S3, receiving an operation of a user for paying attention to the picture file, and establishing an attention list according to the operation, wherein the attention list comprises the picture file and a characteristic value corresponding to the picture file.
In this embodiment, when the user browses the picture file on the electronic device, the user may focus on the picture file by clicking a button (e.g., a thumbs-up button or a favorite button) displayed on an interface (e.g., a lower left corner of the picture) on which the picture file is displayed. And storing the concerned picture file into a database, calculating a characteristic value of the concerned picture file, and establishing the user attention list according to the concerned picture file and the characteristic value.
And S4, acquiring a target picture file currently browsed by the user, and calculating a characteristic value of the target picture file.
In the embodiment, the picture file currently browsed by the user is acquired through a web crawler technology. The method comprises the following specific steps:
1) the method comprises the steps that a web crawler captures web page content currently browsed by a user, wherein the web page content comprises a web page structure; the web page structure includes, but is not limited to, a web page title, web page body content, a picture, sound or video information.
2) And acquiring the target picture file currently browsed by the user according to the webpage structure.
Preferably, after the target picture file currently browsed by the user is acquired, the method further comprises the step of classifying the acquired target picture file, and then different calculation methods are selected according to different categories of the acquired target picture file to calculate the characteristic value of the target picture file.
S5, inquiring the user attention list according to the characteristic value of the target picture file to confirm whether the target picture file is paid attention.
Specifically, when the feature value of the target picture file exists in the user attention list, it is confirmed that the target picture file has been paid attention without repeated attention, and the process goes to step S6;
when the feature value of the target picture file does not exist in the user attention list, it is determined that the target picture file is not paid attention by the user, and the process may be directly ended. That is, the feature value does not exist in the attention list, and when the user has not paid attention to the target picture file before, the process is ended.
It is understood that, when the feature value of the target picture file does not exist in the user attention list, the user may pay attention to the target picture file according to his/her preference.
And S6, when the characteristic value of the target picture file exists in the user attention list, prompting the user not to pay attention to the target picture file.
In this embodiment, the user may be prompted by a message prompt box, a pop-up page, or a voice prompt, etc. without paying attention to the target picture file. For example, a separate page is popped up in an interface where a user browses a webpage currently to display information that the user does not need to pay attention to the target picture file, or a message prompt box is popped up in electronic equipment of the user to display the information that the user does not need to pay attention to the target picture file, or the information that the user does not need to pay attention to the target picture file is directly notified to the user in a voice broadcast mode.
In an embodiment, the method may further include confirming that the target picture file is not paid attention when the feature value of the target picture file does not exist in the user attention list, prompting a user to pay attention to the target picture file, and adding the target picture file and the feature value thereof to the attention list, so that the attention list may be updated.
It can be understood that the user can be prompted to pay attention to the target picture file in a message prompt box, a pop-up page, or a voice prompt, and the like, which is not described herein again.
In summary, the method for intelligently identifying pictures provided by the invention includes receiving picture files; calculating a characteristic value of the picture file; establishing a user attention list, wherein the attention list comprises the characteristic values; acquiring a picture file currently browsed by a user, and calculating a characteristic value of the picture file; comparing whether the characteristic value is consistent with the characteristic value in the attention list; and when the characteristic value is consistent with the characteristic value in the attention list, prompting the user not to pay attention to the picture file. Therefore, the problem that a user pays attention to the same picture for many times when the concerned pictures are too many to identify the same picture issued by different users can be solved. The pictures can be filtered by calculating the characteristic values of the pictures, so that the repeated attention of the same picture is avoided, and the user experience is improved.
The above description is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and it will be apparent to those skilled in the art that modifications may be made without departing from the inventive concept of the present invention, and these modifications are within the scope of the present invention.
The following describes functional modules and hardware structures of an electronic device implementing the above method for intelligently identifying pictures, with reference to fig. 3 to 4.
EXAMPLE III
FIG. 3 is a functional block diagram of an apparatus for intelligently recognizing pictures according to a preferred embodiment of the present invention.
In some embodiments, the means for intelligently identifying pictures 30 operates in a server. The apparatus for intelligently recognizing pictures 30 may include a plurality of functional modules composed of program code segments. The program codes of the various program segments in the smart picture recognition apparatus 30 may be stored in a memory and executed by at least one processor to perform (see fig. 3 and the related description) the smart picture recognition function.
In this embodiment, the apparatus for intelligently recognizing pictures 30 may be divided into a plurality of functional modules according to the functions executed by the apparatus. The functional module may include: the system comprises a building module 301, an obtaining module 302, a calculating module 303, a query module 304 and a prompting module 305. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In some embodiments, the functionality of the modules will be described in greater detail in subsequent embodiments.
The establishing module 301 is configured to receive a picture file.
In this embodiment, a picture file uploaded by an electronic device is received. The electronic device may include, but is not limited to, a Personal Computer (PC), a Personal Digital Assistant (PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a smart phone, and the like. The above mentioned electronic devices are only examples, not exhaustive, and include but not limited to the above mentioned electronic devices.
In this embodiment, an application program is installed on the electronic device, and a user can upload a picture file through the application program.
The application program can be any third-party application installed in an operating system of the electronic device, such as social software capable of browsing pictures, including WeChat, microblog, QQ, Instgram, Tumblr, American show and the like. The present embodiment is not limited to this. The operating system includes an Android system, a saiban system, a Windows system, an ios (mobile operating system developed by apple inc.) system, and the like.
Preferably, after receiving the picture file, the method further comprises the step of classifying the received picture file. The categories of pictures may include, but are not limited to, landscape, people, animals, buildings, and the like.
It is understood that the present scheme can classify the types of the pictures in multiple levels. For example, the picture files are firstly classified into scenery, people, animals, buildings and the like; the image files after the primary classification are classified into a secondary classification, for example, animals are subdivided into cats, dogs, birds, fish, etc., and people are classified into men and women, or into elderly, middle-aged, teenagers, children, etc.
Specifically, the step of classifying the received picture file includes:
a) preprocessing the received picture file; the preprocessing mainly comprises image segmentation, image enhancement, morphological processing and the like.
b) Extracting the characteristics of the preprocessed pictures; the feature extraction is mainly divided into two types: the system comprises bottom layer visual features and intermediate semantic features, wherein the bottom layer visual features mainly comprise simple features such as colors, shapes and textures and complex local invariant features such as SIFT, RIFT and HOG; the middle semantic features mainly comprise semantic attribute features, regional semantic concept features and bag-of-words features.
c) And classifying the pictures after the features are extracted through a classifier. The classifier can help a user to establish an accurate mapping relation between image features and keyword categories and extract image semantic information consistent with user cognition.
Preferably, the method for classifying the image after feature extraction by the classifier includes an image classification method based on a generative model and an image classification method based on a discriminant model. The image classification method based on the generated model is established on the basis of the joint probability distribution of the image characteristics and the image categories, and the image classification method based on the discriminant model is established on the basis of the conditional probability distribution of the image characteristics and the image categories.
The calculating module 303 is configured to calculate a feature value of the picture file.
In this embodiment, after classifying the picture files, different calculation methods may be selected according to different types of the picture files to calculate the feature values of the picture files.
For example, the feature value may be a vector of a certain dimension, such as (P1, P2.., Pn), which is an n-dimensional vector that may be used to describe the shape feature of an object in an image. In the prior art, Hough (Hough) transform may be used to calculate the feature values. The Hough transform is a common algorithm and is not described herein.
In this embodiment, a preferred method for calculating a feature value of a picture file with a first class of a building is described, which may include the following steps:
a1: extracting the image of the contained building from the original image;
a2: filling the boundary of the extracted image with a single color as a background, and enabling the filled image to be a minimum square;
a3: scaling the whole image of the square image into an image with a first preset size in an equal ratio, and dividing the scaled image into sub image blocks with a second preset size;
a4: respectively calculating brightness derivatives of adjacent pixels in horizontal, vertical, positive 45 degrees and negative 45 degrees directions of the sub-image blocks, and taking the number of extreme points of the derivatives in the four directions and the total number of the extreme points on four boundaries of the sub-image blocks as feature vectors of the sub-image blocks;
a5: and taking the feature vectors of all the sub image blocks as the feature vectors of the original image.
The image feature extraction method mainly utilizes the brightness difference between the edge part of an object displayed in an image and the surrounding background and other pixel brightness to find the edge of the object, and can also extract the shape feature of the object in the image, so that the shape feature can be used as the feature value of the image.
In addition, the characteristic value of the picture file with the primary category of people can be calculated by adopting a singular value decomposition method. The method specifically comprises the following steps:
b1: setting A ∈ Rm×nA face gray image is obtained, wherein m is more than or equal to n, and rank (A) r;
b2: two orthogonal matrices and a diagonal matrix are obtained as follows:
U＝[u1,u2,...,um]∈Rm×m,UTU＝I,
the orthogonal matrix is: v ═ V1,v2,...,vn]∈Rn×n,VTV＝I
The diagonal matrix is: s ═ diag [ lambda ]1,λ2,...,λr,0,…,0]∈Rm×n,λ1＞λ2＞…＞λr≥0；
B3: so that the following formula is established,wherein,is ATA and AATCharacteristic value of (u)iAnd viAre each AATAnd ATA corresponds toThe feature vector of (2);
b4: the projection form of the above equation is obtained, i.e. the projection of the image a on U, V is a diagonal matrix S, S ═ UTAnd AV, a vector formed by elements on the diagonal line of S is the singular characteristic value of the image A.
The establishing module 301 is further configured to receive an operation of a user concerning a picture file, and establish an concerning list according to the operation, where the concerning list includes the picture file and a feature value corresponding to the picture file.
In this embodiment, when the user browses the picture file on the electronic device, the user may focus on the picture file by clicking a button (e.g., a thumbs-up button or a favorite button) displayed on an interface (e.g., a lower left corner of the picture) on which the picture file is displayed. And storing the concerned picture file into a database, calculating a characteristic value of the concerned picture file, and establishing the user attention list according to the concerned picture file and the characteristic value.
The obtaining module 302 is configured to obtain a target picture file currently browsed by a user, and calculate a feature value of the target picture file.
In the embodiment, the picture file currently browsed by the user is acquired through a web crawler technology. The method comprises the following specific steps:
1) the method comprises the steps that a web crawler captures web page content currently browsed by a user, wherein the web page content comprises a web page structure; the web page structure includes, but is not limited to, a web page title, web page body content, a picture, sound or video information.
2) And acquiring the target picture file currently browsed by the user according to the webpage structure.
Preferably, after the target picture file currently browsed by the user is acquired, the method further comprises the step of classifying the acquired target picture file, and then different calculation methods are selected according to different categories of the acquired target picture file to calculate the characteristic value of the target picture file.
The query module 304 is configured to query the user attention list according to the feature value of the target picture file to determine whether the target picture file is already attended.
Specifically, when the feature value of the target picture file exists in the user attention list, it is confirmed that the target picture file has been paid attention without repeated attention, and the process goes to step S6;
when the feature value of the target picture file does not exist in the user attention list, it is determined that the target picture file is not paid attention by the user, and the process may be directly ended. That is, the feature value does not exist in the attention list, and when the user has not paid attention to the target picture file before, the process is ended.
It is understood that, when the feature value of the target picture file does not exist in the user attention list, the user may pay attention to the target picture file according to his/her preference.
The prompting module 305 is configured to prompt the user not to focus on the target picture file when the feature value of the target picture file exists in the user focus list.
In this embodiment, the user may be prompted by a message prompt box, a pop-up page, or a voice prompt, etc. without paying attention to the target picture file. For example, a separate page is popped up in an interface where a user browses a webpage currently to display information that the user does not need to pay attention to the target picture file, or a message prompt box is popped up in electronic equipment of the user to display the information that the user does not need to pay attention to the target picture file, or the information that the user does not need to pay attention to the target picture file is directly notified to the user in a voice broadcast mode.
In an embodiment, the prompting module 305 is further configured to confirm that the target picture file is not attended when the feature value of the target picture file is not present in the user attention list, prompt the user to attend to the target picture file, and add the target picture file and the feature value thereof to the attention list, so that the attention list can be updated.
It can be understood that the user can be prompted to pay attention to the target picture file in a message prompt box, a pop-up page, or a voice prompt, and the like, which is not described herein again.
In summary, the apparatus 30 for intelligently identifying pictures provided by the present invention includes an establishing module 301, an obtaining module 302, a calculating module 303, a querying module 304, and a prompting module 305. The establishing module 301 is configured to receive an operation of a user concerning an image file, and establish a user concerning list according to the operation, where the concerning list includes the image file and a feature value corresponding to the image file; the obtaining module 2302 is configured to obtain a target picture file currently browsed by a user, and the calculating module 303 is configured to calculate a feature value of the target picture file; the query module 304 is configured to query the user attention list according to the feature value of the target picture file to determine whether the target picture file is already attended; and the prompting module 305 is configured to confirm that the target picture file is paid attention to when the feature value of the target picture file exists in the user attention list, and prompt the user not to pay attention to the target picture file. Therefore, the problem that a user pays attention to the same picture for many times when the concerned pictures are too many to identify the same picture issued by different users can be solved. The pictures can be filtered by calculating the characteristic values of the pictures, so that the repeated attention of the same picture is avoided, and the user experience is improved.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a dual-screen device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.
Example four
Fig. 4 is a schematic diagram of a server according to a fourth embodiment of the present invention.
The server 4 includes: a database 41, a memory 42, at least one processor 43, a computer program 44 stored in said memory 42 and executable on said at least one processor 43, and at least one communication bus 45.
The at least one processor 43, when executing the computer program 44, performs the steps in the above-described method embodiment of intelligently identifying pictures.
Illustratively, the computer program 44 may be partitioned into one or more modules/units that are stored in the memory 42 and executed by the at least one processor 43 to carry out the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, the instruction segments describing the execution process of the computer program 44 in the server 4.
The server 4 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware thereof includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable gate array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like. Those skilled in the art will appreciate that the schematic diagram 4 is merely an example of the server 4 and does not constitute a limitation of the server 4 and may include more or less components than those shown, or some components in combination, or different components, e.g., the server 4 may also include input output devices, network access devices, buses, etc.
The Database (Database)41 is a repository built on the server 4 that organizes, stores and manages data according to a data structure. Databases are generally classified into hierarchical databases, network databases, and relational databases. In the present embodiment, the database 41 is used to store information such as a picture file and a feature value.
The at least one Processor 43 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The processor 43 may be a microprocessor or the processor 43 may be any conventional processor or the like, and the processor 43 is a control center of the server 4 and connects various parts of the entire server 4 by various interfaces and lines.
The memory 42 may be used for storing the computer program 44 and/or the module/unit, and the processor 43 implements various functions of the server 4 by running or executing the computer program and/or the module/unit stored in the memory 42 and calling data stored in the memory 42. The memory 42 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the server 4, and the like. In addition, the memory 42 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The memory 42 has program code stored therein, and the at least one processor 43 can call the program code stored in the memory 42 to perform related functions. For example, the modules (the establishing module 301, the obtaining module 302, the calculating module 303, the querying module 304 and the prompting module 305) shown in fig. 3 are program codes stored in the memory 42 and executed by the at least one processor 43, so as to realize the functions of the modules for the purpose of intelligently identifying pictures.
The establishing module 301 is configured to receive an operation of a user concerning an image file, and establish a user concerning list according to the operation, where the concerning list includes the image file and a feature value corresponding to the image file;
the obtaining module 302 is configured to obtain a target picture file currently browsed by a user;
the calculation module 303 is configured to calculate a feature value of the target picture file;
the query module 304 is configured to query the user attention list according to the feature value of the target picture file to determine whether the target picture file is already attended; and
the prompting module 305 is configured to confirm that the target picture file is paid attention to when the feature value of the target picture file exists in the user attention list, and prompt the user not to pay attention to the target picture file.
Preferably, before the operation of receiving the user attention picture file and establishing the user attention list according to the operation, the server 4 may further:
receiving a picture file uploaded by electronic equipment;
and calculating the characteristic value of the picture file.
Preferably, after receiving the picture file uploaded by the electronic device, the server 4 may further classify the picture file, which specifically includes:
preprocessing the received picture file;
extracting the characteristics of the preprocessed pictures; and
and classifying the pictures after the features are extracted through a classifier.
Preferably, the calculating the feature value of the picture file includes:
and calculating the characteristic values of the picture files by adopting different calculation methods according to the different types of the classified picture files.
Preferably, the method for classifying the image after feature extraction by the classifier includes an image classification method based on a generative model and an image classification method based on a discriminant model.
Preferably, the acquiring a target picture file currently browsed by a user includes:
the method comprises the steps that a web crawler captures web page content currently browsed by a user, wherein the web page content comprises a web page structure;
and acquiring a target picture file currently browsed by the user according to the webpage structure.
Preferably, when the feature value of the target picture file does not exist in the user attention list, it is determined that the target picture file is not paid attention, the user is prompted to pay attention to the target picture file, and the target picture file and the feature value thereof are added to the attention list.
The modules/units integrated by the server 4 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Although not shown, the server 4 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 43 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The server 4 may further include a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the embodiments are illustrative only and that the scope of the claims is not limited to this configuration.
In the embodiments provided by the present invention, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the above-described embodiments of the electronic device are merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions when the actual implementation is performed.
In addition, functional units in the embodiments of the present invention may be integrated into the same processing unit, or each unit may exist alone physically, or two or more units are integrated into the same unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit scope of the technical solutions of the present invention.
Claims (10)
1. A method for intelligently recognizing pictures, the method comprising:
receiving an operation of a user concerning an image file, and establishing a user concerning list according to the operation, wherein the concerning list comprises the image file and a characteristic value corresponding to the image file;
acquiring a target picture file currently browsed by a user;
calculating a characteristic value of the target picture file;
inquiring the user attention list according to the characteristic value of the target picture file to confirm whether the target picture file is paid attention; and
and when the characteristic value of the target picture file exists in the user attention list, confirming that the target picture file is paid attention to, and prompting the user not to pay attention to the target picture file.
2. The method for intelligently identifying pictures as claimed in claim 1, wherein before said receiving a user attention picture file operation and building a user attention list according to said operation, said method further comprises:
receiving a picture file uploaded by electronic equipment;
and calculating the characteristic value of the picture file.
3. The method for intelligently identifying pictures as claimed in claim 2, wherein after receiving a picture file uploaded by an electronic device, the method further comprises the step of classifying the picture file, the steps comprising:
preprocessing the received picture file;
extracting the characteristics of the preprocessed pictures; and
and classifying the pictures after the features are extracted through a classifier.
4. The method for intelligently identifying pictures according to claim 2, wherein said calculating the feature value of the picture file comprises:
and calculating the characteristic values of the picture files by adopting different calculation methods according to the different types of the classified picture files.
5. The method for intelligently identifying pictures as claimed in claim 2, wherein the method for classifying the pictures after feature extraction by the classifier comprises an image classification method based on a generative model and an image classification method based on a discriminant model.
6. The method for intelligently recognizing pictures as claimed in claim 1, wherein the step of acquiring the target picture file currently browsed by the user comprises:
the method comprises the steps that a web crawler captures web page content currently browsed by a user, wherein the web page content comprises a web page structure;
and acquiring a target picture file currently browsed by the user according to the webpage structure.
7. The method for intelligently identifying pictures as claimed in claim 1, wherein when the feature value of the target picture file does not exist in the user attention list, confirming that the target picture file is not attended, prompting a user to attend to the target picture file, and adding the target picture file and the feature value thereof to the attention list.
8. An apparatus for intelligently recognizing pictures, the apparatus comprising:
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for receiving the operation of the user concerning the picture file and establishing a user concerning list according to the operation, and the concerning list comprises the picture file and a characteristic value corresponding to the picture file;
the acquisition module is used for acquiring a target picture file currently browsed by a user;
the calculation module is used for calculating the characteristic value of the target picture file;
the query module is used for querying the user attention list according to the characteristic value of the target picture file so as to confirm whether the target picture file is concerned; and
and the prompting module is used for confirming that the target picture file is concerned when the characteristic value of the target picture file exists in the user concerned list and prompting the user not to concern the target picture file.
9. A server, characterized in that the server comprises a processor and a memory, the processor being configured to implement the method of intelligently recognizing pictures according to any one of claims 1 to 7 when executing a computer program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of intelligently identifying pictures according to any one of claims 1 to 7.
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| PCT/CN2019/077515 WO2020062788A1 (en) | 2018-09-30 | 2019-03-08 | Method and apparatus for intelligently recognizing picture, server, and storage medium |
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