WO2023178798A1 - Procédé et appareil de classification d'image, et dispositif et support - Google Patents

Procédé et appareil de classification d'image, et dispositif et support Download PDF

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Publication number
WO2023178798A1
WO2023178798A1 PCT/CN2022/090437 CN2022090437W WO2023178798A1 WO 2023178798 A1 WO2023178798 A1 WO 2023178798A1 CN 2022090437 W CN2022090437 W CN 2022090437W WO 2023178798 A1 WO2023178798 A1 WO 2023178798A1
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image
text
features
classified
segmentation
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PCT/CN2022/090437
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English (en)
Chinese (zh)
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唐小初
张祎頔
舒畅
陈又新
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Definitions

  • the present application relates to the field of intelligent decision-making technology of artificial intelligence, and in particular to an image classification method, device, electronic equipment and computer-readable storage medium.
  • image detection such as image classification has become more and more widely used in daily production or life. For example, searching for similar products based on image recognition, in the transportation industry, through crawling and Analyze driving images and automatically identify illegal driving, etc.
  • a single machine learning model has limited image feature representation capabilities and cannot analyze and learn images from multiple aspects.
  • a single machine learning model cannot well combine the advantages of multiple machine learning models with different characteristics, resulting in a single machine learning model. The accuracy of image classification needs to be improved.
  • An image classification method provided by this application includes:
  • image classification analysis is performed on the image to be classified according to the fusion feature and the probability value to obtain a classification result of the image to be classified.
  • This application also provides an image classification device, which includes:
  • a feature extraction module used to obtain the image to be classified, extract the image features of the image to be classified, identify the text content in the image to be classified, and extract the text features of the text content
  • a feature fusion module used to fuse the image features and the text features to obtain fusion features
  • a classification analysis module configured to use a pre-trained activation function to calculate the probability value between the fusion feature and a plurality of preset classification labels, and use a pre-trained integrated classification model to calculate the probability value based on the fusion feature and the probability value.
  • the image to be classified is subjected to image classification analysis to obtain a classification result of the image to be classified.
  • This application also provides an electronic device, which includes:
  • the present application also provides a computer-readable storage medium in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the image classification method as described below. :
  • image classification analysis is performed on the image to be classified according to the fusion feature and the probability value to obtain a classification result of the image to be classified.
  • the embodiment of the present invention uses the fusion feature after fusing image features and text features and the classification probability value corresponding to the fusion feature as the input of the pre-trained integrated classification model.
  • multi-modal fusion features are better than single Modal features have more comprehensive features and higher information value, which can improve the accuracy of image classification.
  • using the classification probability value corresponding to the fusion feature as one of the inputs can improve the accuracy of the pre-trained integrated classification model. Learning efficiency.
  • using the pre-trained integrated classification model can effectively combine the advantages of machine learning models with different features to improve the accuracy of image classification.
  • Figure 1 is a schematic flowchart of an image classification method provided by an embodiment of the present application.
  • Figure 2 is a schematic diagram of a detailed implementation flow of one step in the image classification method provided by an embodiment of the present application
  • Figure 3 is a schematic diagram of a detailed implementation flow of one step in the image classification method provided by an embodiment of the present application.
  • Figure 4 is a functional module diagram of an image classification device provided by an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of an electronic device implementing the image classification method provided by an embodiment of the present application.
  • the embodiment of the present application provides an image classification method.
  • the execution subject of the image classification method includes, but is not limited to, at least one of a server, a terminal, and other electronic devices that can be configured to execute the method provided by the embodiments of the present application.
  • the image classification method can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform.
  • the server may be an independent server, or may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, and content delivery networks (ContentDeliveryNetwork , CDN), as well as cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.
  • Content DeliveryNetwork CDN
  • FIG. 1 is a schematic flowchart of an image classification method provided by an embodiment of the present application.
  • the image classification method includes:
  • the image classification method is explained by taking the classification of products by color based on product images as an example.
  • the images to be classified may be a preset number of product images.
  • the image features of the image to be classified include but are not limited to product outline feature data and product color feature data in the image.
  • a pre-constructed neural network can be used to extract the image features of the image to be classified.
  • the S1 includes:
  • a preset normalization formula can be used to perform a normalization operation on the pixel value of each pixel point in each of the images to be classified, so that the pixel value of each pixel point in the image to be classified is
  • the pixel values are mapped to a preset value range to normalize the color space of the image to be classified to obtain a standard image.
  • the normalization formula can be:
  • Zi is the normalized value of the i-th pixel in the image to be classified
  • xi is the pixel value of the i-th pixel in the image to be classified
  • max(X) is the maximum value in the image to be classified.
  • pixel value, min(X) is the smallest pixel value in the image to be classified.
  • the contrast of the image can be adjusted, and the impact of local shadows and illumination changes on the image features can be reduced, which is beneficial to improving the efficiency of extracting image features. Accuracy.
  • the standard image can be divided into multiple image blocks according to a preset ratio, and the pixel gradient of each pixel in each pixel block can be calculated one by one.
  • the outline of the object in the standard image can be captured. information, while further weakening the interference of lighting and improving the accuracy of image features.
  • a preset gradient algorithm can be used to calculate the pixel gradient of each pixel in each image block.
  • the gradient algorithm includes but is not limited to two-dimensional discrete derivation algorithm, soble operator, etc.
  • Embodiments of the present application can calculate the gradient histogram in each image block based on the pixel gradient, and then use the value of each gradient in the gradient histogram to generate a vector for identifying the gradient histogram, and combine all The vectors of gradient histograms are concatenated into image features of the image to be classified.
  • product display usually uses pictures and text.
  • product-related text description information such as name, specifications, colors, etc. are also provided.
  • a product will contain image features embodied by the product image and textual features provided by the product description information.
  • step S1 since step S1 obtains the image features of the image to be classified, it only performs image analysis on the image to be classified, and does not analyze the text information of the image to be classified. Therefore, in order to improve the classification Regarding the accuracy of image classification, the embodiment of the present application identifies the text content in the image to be classified and analyzes the text content.
  • OCR technology can be used to identify the text content in the image to be classified.
  • using all the word vectors to generate a text vector matrix corresponding to the text content includes: selecting one text segmentation from the plurality of text segmentations one by one as the target segmentation, and counting the target segmentation and all the text segmentations. Describe the number of co-occurrences of adjacent text segments of the target segment within the preset neighborhood range of the target segment; construct a co-occurrence matrix using the number of co-occurrences corresponding to each text segment; splice all the word vectors into Vector matrix; use the co-occurrence matrix and the vector matrix to perform a product operation to obtain a text vector matrix corresponding to the text content.
  • the text content is composed of natural language
  • the text content is directly analyzed, a large amount of computing resources will be occupied, resulting in low efficiency of analysis. Therefore, the text content can be converted into text Vector matrices, thereby converting textual content expressed in natural language into numerical form.
  • a preset standard dictionary can be used to perform word segmentation processing on the text content to obtain multiple text word segments, and the standard dictionary contains multiple standard word segments.
  • the text content is searched in the standard dictionary according to different lengths. If the same standard word segmentation as the text content can be retrieved, it can be determined that the retrieved standard word segmentation is the text of the text content. Participle.
  • the number of co-occurrences corresponding to each text segmentation can be used to construct a co-occurrence matrix as shown below:
  • Xi ,j is the number of co-occurrences of keyword i and adjacent text segment j of keyword i in the text content.
  • models with word vector conversion functions such as word2vec model and NLP (Natural Language Processing) model can be used to respectively convert the multiple text segmentations into word vectors, and then splice the word vectors into the text.
  • the vector matrix of the content is multiplied by the vector matrix and the co-occurrence matrix to obtain a text vector matrix.
  • the text content contains a large number of text segmentations, but not every text segmentation is a feature of the text content, it is necessary to filter the multiple text segmentations.
  • the embodiments of the present application select One text segmentation is selected one by one as the target word segmentation, and the key value of the target word segmentation is calculated according to the word vector of the target word segmentation and the text vector matrix, so as to filter out the content of the text based on the key value. Representative feature word segmentation to obtain the text features of the text content.
  • calculating the key value of the target word segmentation based on the word vector of the target word segmentation and the text vector matrix includes:
  • K is the key value
  • is the text vector matrix
  • T is the matrix transpose symbol
  • is the modulus symbol
  • a preset number of text segmentations are selected from the plurality of text segmentations in descending order according to the key value of each text segmentation as the feature segmentation.
  • the plurality of text segmentation includes: text segmentation A, text segmentation B and text segmentation C.
  • the key value of text segmentation A is 80
  • the key value of text segmentation B is 70
  • the key value of text segmentation C is 30.
  • the preset number is 2, then according to the order of the key values from large to small, select text segmentation A and text segmentation B as the feature segmentation, and carry out the word vectors of the text segmentation A and the text segmentation B. Splicing to obtain the text features of the text content.
  • a pre-built Bert model can be used to extract text features of the text content.
  • feature fusion can be performed before, during and after model training.
  • the fusion of the image features and the text features to obtain the fusion features includes: performing matrix conversion processing on the image features to obtain image features with the same dimensions as the text features; using preset The fully connected layer network associates the text features and the converted image features to obtain fusion features.
  • the dimensions corresponding to the image features and the text features may be different.
  • the dimensions corresponding to the image features and the text features need to be aligned first.
  • the image features can be subjected to matrix transformation processing through the reshape function.
  • the preset fully connected layer network is a convolutional neural network based on deep learning.
  • the following preset fusion function can be used to generate fusion features:
  • F is the fusion feature
  • Q is the converted image feature
  • K is the text feature
  • transpose is the transposition function
  • dot is the matrix multiplication
  • softmax is the activation function
  • dense is the preset fully connected layer
  • the subsequent calculation workload can be reduced, and on the other hand, the effective information of the fused features can be improved. quantity.
  • pre-trained activation functions can be used to calculate the fusion features to calculate the probability value between each feature in the fusion features and a plurality of preset classification labels, where the probability The value refers to the probability value that each feature is a certain classification.
  • the relative probability between a certain feature and a certain classification label is higher, the higher the probability that the feature is used to express the classification label.
  • the activation function includes, but is not limited to, a softmax activation function, a sigmoid activation function, and a relu activation function.
  • the plurality of preset hair-type labels include, but is not limited to, blue, white, yellow, gray, etc.
  • the following activation function can be used to calculate the probability value:
  • x) is the relative probability between the fusion feature x and the classification label a
  • w a is the weight vector of the classification label a
  • T is the transposition operation symbol
  • exp is the expectation operation symbol
  • A The number of preset multiple category labels.
  • S5. Use the pre-trained integrated classification model to perform image classification analysis on the image to be classified according to the fusion feature and the probability value to obtain a classification result of the image to be classified. ;
  • the pre-trained integrated classification model may be a preset number of classifier models built based on the XGBoost (X-GradientBoostingDecisionTree, super gradient boosting tree) integrated learning principle, or may be based on the K-fold voting mechanism A preset number of classifier models are built.
  • XGBoost X-GradientBoostingDecisionTree, super gradient boosting tree
  • the K-fold voting mechanism can be used to perform relevant voting operations based on the classification probability value output by each classifier for each of the images to be classified according to the pre-trained integrated classification. Determine the final classification result of each image to be classified.
  • the pre-trained integrated classification model automatically learns the pre-trained integrated classification using the XGBoost learning principle based on the fusion features of each image to be classified and the probability values corresponding to the fusion features.
  • the weighted probability of each classifier in the model ensures the accuracy of the classification result of the image to be classified.
  • a preset number of classifiers in the pre-trained classification model are used to perform classification analysis on the images to be classified, wherein each classifier outputs a classification probability for each image to be classified. value, the final classification result of each of the images to be classified can be determined based on the XGBoost integrated learning principle, the weights of different classifiers and the classification probability value of each of the classified images.
  • the embodiment of the present application uses the fusion feature after fusing image features and text features and the classification probability value corresponding to the fusion feature as the input of the pre-trained integrated classification model.
  • multi-modal fusion features are better than single Modal features have more comprehensive features and higher information value, which can improve the accuracy of image classification.
  • using the classification probability value corresponding to the fusion feature as one of the inputs can improve the accuracy of the pre-trained integrated classification model. Learning efficiency.
  • using the pre-trained integrated classification model can effectively combine the advantages of machine learning models with different features to improve the accuracy of image classification.
  • FIG. 4 it is a functional module diagram of an image classification device provided by an embodiment of the present application.
  • the image classification device 100 described in this application can be installed in electronic equipment. According to the implemented functions, the image classification device 100 may include a feature extraction module 101, a feature fusion module 102, and a classification analysis module 103.
  • the module described in this application can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of the electronic device and can complete a fixed function, and are stored in the memory of the electronic device.
  • each module/unit is as follows:
  • the feature extraction module 101 is used to obtain the image to be classified, extract the image features of the image to be classified, identify the text content in the image to be classified, and extract the text features of the text content;
  • the feature fusion module 102 is used to fuse the image features and the text features to obtain fusion features;
  • the classification analysis module 103 is configured to use a pre-trained activation function to calculate the probability value between the fusion feature and a plurality of preset classification labels, and use a pre-trained integrated classification model to calculate the probability value based on the fusion feature and the preset classification labels.
  • the probability value is used to perform image classification analysis on the image to be classified, and a classification result of the image to be classified is obtained.
  • each module in the image classification device 100 described in the embodiment of the present application adopts the same technical means as the image classification method described in the above-mentioned Figures 1 to 3 when used, and can produce the same technical effect. I won’t go into details here.
  • FIG. 5 it is a schematic structural diagram of an electronic device for implementing an image classification method provided by an embodiment of the present application.
  • the electronic device 1 may include a processor 10, a memory 11 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an image classification program.
  • the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, CD etc.
  • the memory 11 may be an internal storage unit of the electronic device 1 , such as a mobile hard disk of the electronic device 1 .
  • the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), or a secure digital (SD) equipped on the electronic device 1. card, flash card (FlashCard), etc.
  • the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device.
  • the memory 11 can not only be used to store application software installed on the electronic device 1 and various types of data, such as the code of an image classification program, but can also be used to temporarily store data that has been output or is to be output.
  • the processor 10 may be composed of an integrated circuit, for example, it may be composed of a single packaged integrated circuit, or it may be composed of multiple integrated circuits packaged with the same function or different functions, including one or more Central processing unit (CPU), microprocessor, digital processing chip, graphics processor and various control chip combinations, etc.
  • the processor 10 is the control core (ControlUnit) of the electronic device, using various interfaces and lines to connect various components of the entire electronic device, by running or executing programs or modules (such as image classification) stored in the memory 11 program, etc.), and call the data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
  • the bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus.
  • PCI peripheral component interconnect
  • EISA extended industry standard architecture
  • the bus can be divided into address bus, data bus, control bus, etc.
  • the bus is configured to enable connection communication between the memory 11 and at least one processor 10 and the like.
  • FIG. 5 only shows an electronic device with components. Persons skilled in the art can understand that the structure shown in FIG. 5 does not limit the electronic device 1 and may include fewer or more components than shown in the figure. components, or combinations of certain components, or different arrangements of components.
  • the electronic device 1 may also include a power supply (such as a battery) that supplies power to various components.
  • the power supply may be logically connected to the at least one processor 10 through a power management device, so that through the power management device
  • the device implements functions such as charging management, discharge management, and power consumption management.
  • the power supply may also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.
  • the electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described again here.
  • the electronic device 1 may also include a network interface.
  • the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which are usually used in the electronic device. 1. Establish communication connections with other electronic devices.
  • the electronic device 1 may also include a user interface, which may be a display (Display) or an input unit (such as a keyboard).
  • the user interface may also be a standard wired interface or a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, or the like.
  • the display may also be appropriately referred to as a display screen or a display unit, and is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
  • the image classification program stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can realize:
  • image classification analysis is performed on the image to be classified according to the fusion feature and the probability value to obtain a classification result of the image to be classified.
  • the integrated modules/units of the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the computer-readable storage medium may be volatile or non-volatile.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) ).
  • This application also provides a computer-readable storage medium, which may be volatile or non-volatile.
  • the readable storage medium stores a computer program. When executed by the processor of the electronic device, the computer program can implement:
  • image classification analysis is performed on the image to be classified according to the fusion feature and the probability value to obtain a classification result of the image to be classified.
  • modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional module in various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or in the form of hardware plus software function modules.
  • Blockchain is a new application model of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain is essentially a decentralized database. It is a series of data blocks generated using cryptographic methods. Each data block contains a batch of network transaction information and is used to verify its Validity of information (anti-counterfeiting) and generation of the next block.
  • Blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
  • AI artificial intelligence
  • digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

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Abstract

L'invention concerne un procédé de classification d'image et un appareil de classification d'image. Le procédé de classification d'image consiste à : extraire une caractéristique d'image et une caractéristique de texte d'une image à classer (S1, S2) ; fusionner la caractéristique d'image et la caractéristique de texte afin d'obtenir une caractéristique fusionnée (S3) ; et calculer, à l'aide d'une fonction d'activation pré-apprise, des valeurs de probabilité de la caractéristique fusionnée par rapport à une pluralité d'étiquettes de classification prédéfinies (S4) ; et effectuer une analyse de classification d'image sur ladite image selon la caractéristique fusionnée et les valeurs de probabilité et en utilisant un modèle de classification intégré pré-appris afin d'obtenir un résultat de classification de ladite image (S5), ce qui permet d'améliorer la précision et l'efficacité de la classification d'image.
PCT/CN2022/090437 2022-03-25 2022-04-29 Procédé et appareil de classification d'image, et dispositif et support WO2023178798A1 (fr)

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