WO2021164550A1 - Procédé et appareil de classification d'images - Google Patents

Procédé et appareil de classification d'images Download PDF

Info

Publication number
WO2021164550A1
WO2021164550A1 PCT/CN2021/075045 CN2021075045W WO2021164550A1 WO 2021164550 A1 WO2021164550 A1 WO 2021164550A1 CN 2021075045 W CN2021075045 W CN 2021075045W WO 2021164550 A1 WO2021164550 A1 WO 2021164550A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
classified
clustering
global feature
feature
Prior art date
Application number
PCT/CN2021/075045
Other languages
English (en)
Chinese (zh)
Inventor
孙哲
Original Assignee
Oppo广东移动通信有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Oppo广东移动通信有限公司 filed Critical Oppo广东移动通信有限公司
Publication of WO2021164550A1 publication Critical patent/WO2021164550A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques

Definitions

  • This application relates to the field of image processing technology, and in particular to an image classification method and device.
  • the embodiments of the present application provide an image classification method and device, which can reduce the amount of calculation for image classification, and provide a possible way for images with a large amount of calculation to run on terminal hardware.
  • an embodiment of the present application provides an image classification method, and the method includes:
  • the classification result of the image to be classified is determined according to the global feature.
  • an image classification device the device including:
  • the acquiring unit is used to acquire the image to be classified
  • a dividing unit configured to divide the image to be classified to obtain M partial image blocks, where M is a positive integer greater than 1;
  • a clustering unit configured to cluster the M partial image blocks to obtain N clustering results, where N is a positive integer greater than 1;
  • a first determining unit configured to determine a global feature of the image to be classified according to the M local image blocks and the N clustering results, where the global feature is a feature vector of the image to be classified;
  • the second determining unit is configured to determine the classification result of the image to be classified according to the global feature.
  • an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured by The processor executes, and the program includes instructions for executing the steps in the method described in the first aspect of the embodiments of the present application.
  • an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute Some or all of the steps described in one aspect.
  • the embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute as implemented in this application.
  • the computer program product may be a software installation package.
  • this application can obtain the image to be classified, divide the image to be classified to obtain M partial image blocks, and cluster the M partial image blocks to obtain N clustering results.
  • the M local image blocks and the N clustering results determine the global feature of the image to be classified, and determine the classification result of the image to be classified according to the global feature.
  • FIG. 1 is a schematic flowchart of an image classification method provided by an embodiment of the present application
  • FIG. 2 is a schematic diagram of an image division process provided by an embodiment of the present application.
  • FIG. 3 is a schematic flowchart of an image clustering provided by an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a process for obtaining image feature vectors according to an embodiment of the present application.
  • Fig. 5 is a functional unit composition diagram of another image classification device provided by an embodiment of the present application.
  • Fig. 6 is a schematic structural diagram of a terminal device provided by an embodiment of the present application.
  • the terminal devices described in the embodiments of the present application include but are not limited to other portable devices such as mobile phones, laptop computers, or tablet computers with touch-sensitive surfaces (for example, touch screen displays and/or touch pads).
  • the device is not a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., touch screen display and/or touch pad).
  • a terminal device including a display and a touch-sensitive surface is described.
  • the terminal device may include one or more other physical user interface devices such as a physical keyboard, mouse, and/or joystick.
  • the terminal device supports various applications, such as one or more of the following: drawing application, presentation application, word processing application, website creation application, disk burning application, spreadsheet application, game application, phone Apps, video conferencing apps, email apps, instant messaging apps, exercise support apps, photo management apps, digital camera apps, digital camera apps, web browsing apps, digital music player apps And/or digital video player application.
  • applications such as one or more of the following: drawing application, presentation application, word processing application, website creation application, disk burning application, spreadsheet application, game application, phone Apps, video conferencing apps, email apps, instant messaging apps, exercise support apps, photo management apps, digital camera apps, digital camera apps, web browsing apps, digital music player apps And/or digital video player application.
  • Various application programs that can be executed on the terminal device can use at least one common physical user interface device such as a touch-sensitive surface.
  • One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal can be adjusted and/or changed between applications and/or within corresponding applications.
  • the common physical architecture of the terminal for example, a touch-sensitive surface
  • this application proposes an image classification method, which obtains an image to be classified, divides the image to be classified to obtain M partial image blocks, and clusters the M partial image blocks to obtain N Clustering results, determining the global feature of the image to be classified according to the M local image blocks and the N clustering results, and determining the classification result of the image to be classified according to the global feature, thereby reducing image classification
  • the amount of calculation is realized on the terminal device to run the image classification of the large amount of calculation or the high-resolution image.
  • FIG. 1 is a schematic flowchart of an image classification method provided by an embodiment of the present application.
  • the image classification method is applied to a terminal device.
  • the image classification method may include the following steps:
  • the image to be classified can be obtained locally from the terminal device, and the image to be classified can also be received from other devices, which is not limited here.
  • Obtaining the image to be classified locally from the terminal device may refer to obtaining the image to be classified from the memory of the terminal device, or it may be obtaining the photos that have not been stored in the memory when the terminal device is taking a picture.
  • the image to be classified is a photo taken by the terminal device that has not been stored in the memory, it can be realized that the photo is stored in the memory to complete the classification of the photo without subsequent classification.
  • the image to be classified may refer to an image of the category to be detected.
  • An image usually includes a subject and a background.
  • the subject is the main object of the image.
  • the background is the scene that sets off the subject in the image.
  • the category of the image is determined according to the subject in the image. For example, if the subject in the image is a building, then the category of the image is the architectural category; The subject of the image is green plants, so the category of the image is green plants.
  • the image to be detected may be an image obtained by various applications executed on the terminal device, for example, a drawing application, a survey application, a word processing application, a photo management application, etc.
  • Different applications or application scenarios have different subjects and backgrounds in the image.
  • the subject can include entities representing geographic features such as buildings, roads, trees, and rivers
  • word processing applications the main body mainly includes text. Therefore, the terminal device can first perform a simple classification of the image to be classified according to the application and/or application scenario to which the image to be classified belongs, that is, determine the application and/or application scenario to which the image to be classified belongs according to the source of the image to be classified. This simplifies the processing of images to be classified.
  • the method before dividing the image to be classified, the method further includes: acquiring an original image, and preprocessing the original image to obtain the image to be classified.
  • the original image can also be randomly selected from the image library. There is no restriction on this.
  • the original image size can be cropped into a uniform format.
  • the image to be classified can be uniformly cropped to a size of 512 ⁇ 512, and The original image is normalized to obtain the image to be classified.
  • the terminal device may compress the original image first, thereby further reducing the need for processing.
  • the amount of calculation to classify images may be performed.
  • the terminal device may divide the image to be classified into M identical partial image blocks according to a preset image size. For example, as shown in FIG. 2, the image to be classified can be divided into 9 rectangular partial image blocks in a nine-square grid manner.
  • the terminal device can also divide the image to be classified into M partial image blocks of the same or different sizes according to a preset image size list or pattern; or, the terminal device can also randomly divide the image to be classified into M partial image blocks.
  • the application embodiment does not limit this.
  • the embodiment of the present application can perform image classification by using M partial image blocks as input data, which not only increases the diversity of data, but also improves the robustness of the image classification model.
  • the size of the partial image blocks can be set according to the scene to which the image belongs, and the scene to which the image belongs can be divided according to whether the scene characteristic of the image is fixed.
  • the first scene is the scene characteristic.
  • the second scene is an image scene with fixed scene characteristics, such as a plant scene.
  • images belonging to natural scenes usually have no clear and fixed scene features.
  • the size list is used to extract the local features of the image belonging to the plant scene, because the size in the size list may vary greatly, the extracted local features may not be obvious , It is easy to be disturbed by other information (such as background information), such as an image.
  • the content contains three or four flowers in a large patch of grass. It belongs to the flower cluster and does not belong to the grass. If you use the size list to extract the local features of the image, you can judge the image The category of the image may be judged as grass.
  • the scene feature may refer to the image feature that can characterize the scene to which the image belongs. Fixed scene features may mean that the distribution of scene features in the image is relatively concentrated and not scattered; unfixed scene features may mean that the distribution of scene features in the image is relatively scattered and not concentrated.
  • the scene of the image to be tested may refer to the scene of the subject in the image to be tested.
  • the scene to which the image belongs is a plant scene
  • the subject of the image is a beach, valley, etc.
  • the scene to which the image belongs is a natural scene.
  • the partial image blocks do not overlap each other, and the shape blocks of the partial image blocks may be rectangular or irregular polygons.
  • the embodiment of the present application does not limit the shape of the partial image blocks here.
  • the terminal device may determine the size of M according to the resolution of the image to be classified, and the resolution of the image to be classified may have a proportional relationship or a mapping relationship with the size of M, that is, the size of the image to be classified.
  • the resolution of the image to be classified may have a proportional relationship or a mapping relationship with the size of M, that is, the size of the image to be classified.
  • the terminal device can determine the shape and quantity of the partial image block according to the application and/or application scenario to which the image to be classified belongs. For example, for a remote sensing image generated by a survey application, the height The feature distribution of different categories of images in the resolution image is relatively concentrated, that is, subjects belonging to the same category are concentrated on the entire image, and the terminal device can divide the image to be classified into multiple partial image blocks of the same rectangular size; for photographing applications The image of a person is generated in the program, and the main body of the image is mostly distributed in the middle of the whole image. The terminal device can be divided in order from the middle, and the size of the partial image block in the middle part can be larger, so that the partial image block contains more features .
  • the clustering of the M partial image blocks to obtain N clustering results includes: clustering the M partial image blocks using an unsupervised learning clustering method to obtain the N
  • the N is a positive integer greater than 1.
  • a classification model needs to be used to obtain the local features of the partial image blocks. Therefore, it is necessary to train the classification model first, and use the trained classification model to obtain the local features of the local image block.
  • training the classification training model first divide the training samples to obtain multiple partial training samples, then sample the partial training samples, input the sampled training samples into the classification model, and the classification model outputs the local characteristics of the training samples , Use the loss function to train back.
  • the classification model may be used to extract the features of the M partial image blocks respectively to obtain the M partial features.
  • the present application may use a combined partial feature formed by combining multiple features To perform cluster analysis.
  • image local features can include color features, LBP features, texture features, etc. Among them, the color features of the image have less dependence on the size, direction, and viewing angle of the image itself.
  • the commonly used color histogram features describe the different colors.
  • the proportion of the whole image; texture is an important spatial information of remote sensing images. With the increase of resolution, the internal structure of the ground objects becomes clearer and clearer, which is manifested as the texture structure of the ground objects in the remote sensing image.
  • texture features can reflect the regular spatial changes of pixels in the target object. Therefore, the terminal device can select different features or feature combinations according to different application scenarios or applications. For example, remote sensing images can use color features and texture features to combine to form combined local features; or select local features based on preset feature options. Features or local feature combinations are not limited in the embodiment of the present application.
  • the combined local features can be one-dimensional, that is, multiple combined features can be spliced.
  • the texture feature can be spliced behind the color feature;
  • the combined local feature can also be multi-dimensional, that is, multiple combined features can be A feature matrix, which is not limited in the embodiment of the present application.
  • the color features are based on the HSL (Hue, Saturation, Lightness) color space, and the color histogram features are extracted. Compared with the RGB color space, the HSL color space is more in line with the visual perception characteristics of the human eye.
  • HSL Human, Saturation, Lightness
  • the partial image blocks belonging to the same category in the M partial image blocks are clustered together based on the extracted M partial features, and N clustering results are obtained.
  • the size of the M and N depends on the number of categories contained in the M partial image blocks.
  • the number in the partial image block represents the category of the partial image block
  • the 9 partial image blocks Perform clustering, cluster the partial image blocks of the same category into the same class, and obtain 4 clustering results.
  • the clustering result 1 contains 3 partial image blocks
  • the clustering results 2-4 contain 2 partial image blocks respectively.
  • Image block
  • the clustering methods of unsupervised learning include but are not limited to: K-means clustering algorithm, Birch clustering algorithm, DBSCAN clustering algorithm and K nearest neighbor classification algorithm.
  • a good clustering division should reflect the internal structure of the data set as much as possible, so that the categories within the same category are as the same as possible, and the categories between the categories are as different as possible.
  • K-means clustering algorithm takes the K-means clustering algorithm as an example. From the perspective of distance, clusters with extremely small intra-class distances and large inter-class distances are the optimal clusters.
  • the clusters with similar local features are divided into the same class as much as possible. Dissimilar local features are divided into different categories as much as possible.
  • the global feature is the feature vector of the image to be classified
  • both the global feature and the local feature are the image features of the image to be classified
  • the global feature refers to the feature vector extracted from the entire image to be tested, which is derived from the entire The feature of an image to be tested
  • the local feature refers to a feature vector extracted from a partial image block of the entire image to be tested, and is a feature from a partial image block of the image to be tested.
  • the present application may use a combined global feature formed by a combination of multiple features to perform image classification.
  • Image global features can include color features, LBP features, texture features, etc.
  • the terminal device can select different features or feature combinations according to different application scenarios or applications. For example, remote sensing images can use color features and texture features to combine to form a combined global Features; global features or global feature combinations can also be selected according to preset feature options, which are not limited in the embodiment of the present application.
  • the determining the global feature of the image to be classified according to the M local image blocks and the N clustering results includes: separately clustering the M local image blocks with the N clustering results Each clustering result in the result is subjected to a convolution operation to obtain a first feature vector; binary image coding is performed on the first feature vector to obtain the global feature.
  • 9 partial image blocks are respectively convolved with 4 clustering results obtained by clustering the 9 partial image blocks to obtain the first feature vector of the image to be classified.
  • the first feature vector is used to describe the global information of the image to be classified.
  • the first feature vector may be one-dimensional, and the feature vector obtained by convolution of the partial image block and the clustering result may be spliced to obtain the first feature vector; the first feature vector may also be multi-dimensional.
  • the application embodiment does not limit this.
  • the first feature vector may also be one or more.
  • performing a convolution operation on each of the M partial image blocks and each of the N clustering results may be to combine the local features extracted from the partial image blocks with the clustering results.
  • the local features extracted from the class result are convolved to obtain the first feature vector.
  • the performing binary image encoding on the first feature vector to obtain the global feature includes: setting a value greater than the first value in the first feature vector as a first value according to a binary image encoding rule. Two values, and setting a value in the first feature vector that is less than or equal to the first value as the first value to obtain the global feature.
  • the first value can be 0, and the second value can be 1; the first value can also be 255, and the second value can be 0.
  • each pixel of the image can be between (0,255).
  • Each pixel on the grayscale image has only two possible values or grayscale states.
  • the above global feature can be represented by 8 bits. Characteristics.
  • the first value and the second value may also be other values, which are not limited in the embodiment of the present application.
  • the convolution operation before performing the convolution operation on the M partial image blocks and each of the N clustering results respectively, it is necessary to use a classification model to obtain the partial image blocks and clusters.
  • the local features of the result can be obtained by using the above classification model to obtain the local features of the local image block and the clustering result.
  • the terminal device may input the partial image block and the clustering result into the preset algorithm, and use the preset algorithm to process the image.
  • the preset algorithm may be Fast (Fast Region Based Convolutional Neural Network, RCNN) algorithm.
  • RCNN Fast Region Based Convolutional Neural Network
  • the user can pre-set the convolution window in the Fast RCNN algorithm.
  • the terminal uses the convolution window to convolve the image to obtain the first Feature vector.
  • the first feature vector refers to a complete matrix obtained after convolving the image.
  • the determining the classification result of the image to be classified according to the global feature includes: performing a convolution operation on the global feature and a convolution vector to obtain a probability vector of the global feature, and the convolution vector It is obtained by training the image samples of the labeled category; the category corresponding to the maximum value in the probability vector is used as the classification result of the image to be classified.
  • the size of the above convolution operation is 1x1
  • the value in the above probability vector represents the probability that the image to be classified belongs to each category. According to the value of the probability vector, the category corresponding to the maximum value can be used as the category to be classified. The classification result of the image.
  • the original training samples of each category in the image library can be used for training to obtain the convolution vector, or the collection of image samples marked with image categories can be trained to obtain the convolution vector.
  • the product vector is not limited in the embodiment of this application. It should be noted that the above-mentioned convolution vector may depend on the image samples of the annotated image category. For different image samples of the annotated image category, the convolution vector may be different. Therefore, the terminal device can select the corresponding image according to the application scenario or application.
  • the convolutional vector can be obtained by training with adapted image samples. For example, for the observing terminal equipment, in order to accurately classify remote sensing images and provide detailed ground information, you can directly use the remote sensing image samples with marked categories for training to obtain the convolution vector , Thereby improving the accuracy of image classification.
  • the global feature and the convolution vector can directly obtain the probability vector of the image to be classified after the convolution operation. Due to the small amount of 1x1 convolution calculation, the parallelization acceleration is obvious, which greatly reduces the calculation amount of the original data and the memory usage Therefore, the image classification algorithm is optimized under the condition of ensuring the accuracy, and the calculation cost of the unsupervised learning method is greatly reduced under the premise of ensuring the accuracy of the image classification.
  • the method further includes: acquiring a first image set, the first image set including images of an already-labeled image category; using the first image set to train the classifier to be trained , Get the first classifier;
  • the determining the classification result of the image to be classified according to the global feature includes: inputting the global feature into the first classifier, and outputting the classification result of the image to be classified.
  • inputting the global features of the image to be classified into the classifier can enable the classifier to classify according to the global features of the image to be classified. sort.
  • the classifier before using the classifier to obtain the category of the image to be classified, the classifier needs to be trained first, and the trained classifier is used to obtain the category of the image to be classified. : When training the classifier, use the trained classifier to obtain the global features of the images in the first image set, input the global features to the classifier, the classifier outputs the category of the images in the first image set, and use target supervision to control the classifier Perform backhaul training.
  • target supervision is supervised learning in deep learning, such as loss function.
  • the classifier may refer to a model that classifies the image to be classified according to the image characteristics of the image to be classified.
  • the classifier in this application may be a non-linear classifier, such as a non-linear support vector machine (Support Vector Machine, SVM).
  • SVM Simple Vector Machine
  • Non-linear classifiers can effectively expand the classification dimension and reduce the defects of linear classifiers such as softmax and fully connected layers in non-linear classification.
  • the clustering results of local image blocks are obtained by the clustering method of unsupervised learning, the classification model of supervised learning is used to obtain the global features of the image to be classified, and the classification model of supervised learning is used according to the global features. Determine the category of the image to be classified.
  • This application improves the performance of the image classification algorithm and reduces power consumption by combining unsupervised learning and supervised deep learning algorithms.
  • the first classifier and the convolution vector may be obtained by training the classifier to be trained through one image sample, or the first classifier and the convolution vector may be obtained by training the classifier to be trained through different image samples.
  • the embodiment of the application does not limit this.
  • the embodiment of the application proposes an image classification method, which is applied to a terminal device.
  • the image to be classified is divided to obtain M partial image blocks, and the M partial image blocks are Perform clustering to obtain N clustering results, determine the global feature of the image to be classified according to the M local image blocks and the N clustering results, and determine the global feature of the image to be classified according to the global feature
  • the classification result reduces the amount of redundant calculation for image classification, realizes the simplification and acceleration of the algorithm, and provides a possible way for the image with a large amount of calculation to run on the terminal hardware.
  • an electronic device includes hardware structures and/or software modules corresponding to each function.
  • this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.
  • the embodiment of the present application may divide the electronic device into functional units according to the foregoing method examples.
  • each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation.
  • FIG. 5 is a block diagram of the functional unit composition of an image classification device provided by an embodiment of the present application, which is applied to a terminal device. As shown in FIG. 5, the device includes:
  • the obtaining unit 510 is configured to obtain the image to be classified
  • the dividing unit 520 is configured to divide the image to be classified to obtain M partial image blocks, where M is a positive integer greater than 1;
  • the clustering unit 530 is configured to cluster the M partial image blocks to obtain N clustering results, where N is a positive integer greater than 1;
  • the first determining unit 540 is configured to determine a global feature of the image to be classified according to the M local image blocks and the N clustering results, where the global feature is a feature vector of the image to be classified;
  • the second determining unit 550 is configured to determine the classification result of the image to be classified according to the global feature.
  • the first determining unit 540 is specifically configured to: perform a convolution operation on the M partial image blocks and each of the N clustering results. , Obtain a first feature vector; perform binary image coding on the first feature vector to obtain the global feature.
  • the first determining unit 540 is further specifically configured to: set a value greater than the first value in the first feature vector to a second value according to a binary image coding rule, And setting a value in the first feature vector that is less than or equal to the first value as the first value to obtain the global feature.
  • the second determining unit 550 is specifically configured to: perform a convolution operation on the global feature and the convolution vector to obtain the probability vector of the global feature, and the convolution vector It is obtained by training the image samples of the labeled category; the category corresponding to the maximum value in the probability vector is used as the classification result of the image to be classified.
  • the size of the convolution operation is 1 ⁇ 1.
  • the obtaining unit 510 is further configured to: obtain a first image set, where the first image set includes images with annotated image categories.
  • the device further includes a training unit 560 configured to train the classifier to be trained using the first image set to obtain the first classifier.
  • the second confirmation unit 550 is further specifically configured to: input the global feature into the first classifier, and output the classification result of the image to be classified.
  • the clustering unit 530 is specifically configured to use an unsupervised learning clustering method to cluster the M partial image blocks to obtain the N clustering results.
  • the obtaining unit before the dividing the image to be classified, is further configured to: obtain an original image, and preprocess the original image to obtain the image to be classified.
  • the embodiment of the present application proposes an image classification device, which is applied to a terminal device.
  • the image to be classified is divided to obtain M partial image blocks, and the M partial image blocks are Perform clustering to obtain N clustering results, determine the global feature of the image to be classified according to the M local image blocks and the N clustering results, and determine the global feature of the image to be classified according to the global feature
  • the classification result reduces the amount of redundant calculation for image classification, realizes the simplification and acceleration of the algorithm, and provides a possible way for the image with a large amount of calculation to run on the terminal hardware.
  • FIG. 6 is a schematic structural diagram of a terminal device provided by an embodiment of the present application.
  • the terminal device includes one or more processors, one or more memories, and one or more communications. Interface, and one or more programs;
  • the one or more programs are stored in the memory, and are configured to be executed by the one or more processors;
  • the program includes instructions for performing the following steps:
  • the classification result of the image to be classified is determined according to the global feature.
  • the program includes instructions that are further used to perform the following steps: respectively, the M partial image blocks are collated with each of the N clustering results.
  • the program includes instructions that are further used to perform the following steps: according to a binary image coding rule, a value greater than the first value in the first feature vector is set to a second value , And setting a value in the first feature vector that is less than or equal to the first value as the first value to obtain the global feature.
  • the program includes instructions that are further used to perform the following steps: perform a convolution operation on the global feature and the convolution vector to obtain the probability vector of the global feature, and the volume
  • the product vector is obtained by training image samples of the labeled category; the category corresponding to the maximum value in the probability vector is used as the classification result of the image to be classified.
  • the size of the convolution operation is 1 ⁇ 1.
  • the program includes instructions that are further used to perform the following steps: obtaining a first image set, the first image set including images of an already-labeled image category; using the first image Set the classifier to be trained to obtain the first classifier.
  • the program includes instructions that are further used to perform the following steps: input the global feature to the first classifier, and output the classification result of the image to be classified.
  • the program includes instructions that are further used to perform the following steps: clustering the M partial image blocks using an unsupervised learning clustering method to obtain the N clusters Class result.
  • the program before dividing the image to be classified, includes instructions for performing the following steps: obtaining an original image, and preprocessing the original image to obtain the image to be classified. Categorize images.
  • the embodiment of the present application proposes an image classification device, which is applied to a terminal device.
  • the image to be classified is divided to obtain M partial image blocks, and the M partial image blocks are Perform clustering to obtain N clustering results, determine the global feature of the image to be classified according to the M local image blocks and the N clustering results, and determine the global feature of the image to be classified according to the global feature
  • the classification result reduces the amount of redundant calculation for image classification, realizes the simplification and acceleration of the algorithm, and provides a possible way for the image with a large amount of calculation to run on the terminal hardware.
  • An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method as recorded in the above method embodiment ,
  • the above-mentioned computer includes terminal equipment.
  • the embodiments of the present application also provide a computer program product.
  • the above-mentioned computer program product includes a non-transitory computer-readable storage medium storing a computer program. Part or all of the steps of the method.
  • the computer program product may be a software installation package, and the above-mentioned computer includes terminal equipment.
  • the disclosed device may be implemented in other ways.
  • the device embodiments described above are only illustrative, for example, the division of the above-mentioned units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or integrated. To another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
  • the units described above as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable memory.
  • the technical solution of the present application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory.
  • a number of instructions are included to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the foregoing methods of the various embodiments of the present application.
  • the aforementioned memory includes: U disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.
  • the program can be stored in a computer-readable memory, and the memory can include: flash memory, ROM, RAM, magnetic disk or CD, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Analysis (AREA)

Abstract

Les modes de réalisation de la présente invention concernent un procédé et un appareil de classification d'images, qui sont appliqués à un dispositif terminal. Ledit procédé comprend : l'acquisition d'une image à classifier ; la division de ladite image pour obtenir M blocs d'image locaux ; le groupement des M blocs d'image locaux pour obtenir N résultats de regroupement ; la détermination d'une caractéristique globale de ladite image selon les M blocs d'image locaux et les N résultats de regroupement ; et la détermination d'un résultat de classification de ladite image selon la caractéristique globale. La présente invention peut réduire les calculs redondants d'une classification d'images, réalise un algorithme simplifié et accéléré, et permet la possibilité d'exécuter une image ayant une grande quantité de calculs sur le matériel d'un terminal.
PCT/CN2021/075045 2020-02-18 2021-02-03 Procédé et appareil de classification d'images WO2021164550A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010101515.6 2020-02-18
CN202010101515.6A CN111325271B (zh) 2020-02-18 2020-02-18 图像分类方法及装置

Publications (1)

Publication Number Publication Date
WO2021164550A1 true WO2021164550A1 (fr) 2021-08-26

Family

ID=71168841

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/075045 WO2021164550A1 (fr) 2020-02-18 2021-02-03 Procédé et appareil de classification d'images

Country Status (2)

Country Link
CN (1) CN111325271B (fr)
WO (1) WO2021164550A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116206208A (zh) * 2023-05-05 2023-06-02 河东区志远苗木种植专业合作社 一种基于人工智能的林业病虫害快速分析系统
CN116612389A (zh) * 2023-07-20 2023-08-18 青建国际集团有限公司 一种建筑施工进度管理方法及系统

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111325271B (zh) * 2020-02-18 2023-09-12 Oppo广东移动通信有限公司 图像分类方法及装置
CN111881849A (zh) * 2020-07-30 2020-11-03 Oppo广东移动通信有限公司 图像场景检测方法、装置、电子设备及存储介质
CN111652329B (zh) * 2020-08-05 2020-11-10 腾讯科技（深圳）有限公司 一种图像分类方法、装置、存储介质和电子设备
CN113112518B (zh) * 2021-04-19 2024-03-26 深圳思谋信息科技有限公司 基于拼接图像的特征提取器生成方法、装置和计算机设备
CN114418030B (zh) * 2022-01-27 2024-04-23 腾讯科技（深圳）有限公司 图像分类方法、图像分类模型的训练方法及装置
CN114743247A (zh) * 2022-04-26 2022-07-12 支付宝(杭州)信息技术有限公司 一种面部识别模型的训练方法、装置以及设备

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7840071B2 (en) * 2006-12-12 2010-11-23 Seiko Epson Corporation Method and apparatus for identifying regions of different content in an image
CN104077597A (zh) * 2014-06-25 2014-10-01 小米科技有限责任公司 图像分类方法及装置
CN109410196A (zh) * 2018-10-24 2019-03-01 东北大学 基于泊松环形条件随机场的宫颈癌组织病理图像诊断方法
CN110751218A (zh) * 2019-10-22 2020-02-04 Oppo广东移动通信有限公司 图像分类方法、图像分类装置及终端设备
CN111325271A (zh) * 2020-02-18 2020-06-23 Oppo广东移动通信有限公司 图像分类方法及装置

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008282267A (ja) * 2007-05-11 2008-11-20 Seiko Epson Corp シーン識別装置、及び、シーン識別方法
CN104517113B (zh) * 2013-09-29 2017-12-19 浙江大华技术股份有限公司 一种图像的特征提取方法、图像的分类方法及相关装置
CN103942564B (zh) * 2014-04-08 2017-02-15 武汉大学 基于非监督特征学习的高分辨率遥感影像场景分类方法
CN104036293B (zh) * 2014-06-13 2017-02-22 武汉大学 基于快速二值编码的高分辨率遥感影像场景分类方法
CN107368791A (zh) * 2017-06-29 2017-11-21 广东欧珀移动通信有限公司 虹膜活体检测方法及相关产品
CN110580482B (zh) * 2017-11-30 2022-04-08 腾讯科技（深圳）有限公司 图像分类模型训练、图像分类、个性化推荐方法及装置
CN108647602B (zh) * 2018-04-28 2019-11-12 北京航空航天大学 一种基于图像复杂度判定的航空遥感图像场景分类方法
CN109800781A (zh) * 2018-12-07 2019-05-24 北京奇艺世纪科技有限公司 一种图像处理方法、装置及计算机可读存储介质
CN110309865A (zh) * 2019-06-19 2019-10-08 上海交通大学 一种无人机巡视输电线路销钉缺陷分级图像识别方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7840071B2 (en) * 2006-12-12 2010-11-23 Seiko Epson Corporation Method and apparatus for identifying regions of different content in an image
CN104077597A (zh) * 2014-06-25 2014-10-01 小米科技有限责任公司 图像分类方法及装置
CN109410196A (zh) * 2018-10-24 2019-03-01 东北大学 基于泊松环形条件随机场的宫颈癌组织病理图像诊断方法
CN110751218A (zh) * 2019-10-22 2020-02-04 Oppo广东移动通信有限公司 图像分类方法、图像分类装置及终端设备
CN111325271A (zh) * 2020-02-18 2020-06-23 Oppo广东移动通信有限公司 图像分类方法及装置

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116206208A (zh) * 2023-05-05 2023-06-02 河东区志远苗木种植专业合作社 一种基于人工智能的林业病虫害快速分析系统
CN116612389A (zh) * 2023-07-20 2023-08-18 青建国际集团有限公司 一种建筑施工进度管理方法及系统
CN116612389B (zh) * 2023-07-20 2023-09-19 青建国际集团有限公司 一种建筑施工进度管理方法及系统

Also Published As

Publication number Publication date
CN111325271A (zh) 2020-06-23
CN111325271B (zh) 2023-09-12

Similar Documents

Publication Publication Date Title
WO2021164550A1 (fr) Procédé et appareil de classification d'images
US10609286B2 (en) Extrapolating lighting conditions from a single digital image
CN112651438A (zh) 多类别图像的分类方法、装置、终端设备和存储介质
US11704357B2 (en) Shape-based graphics search
CN106874937B (zh) 一种文字图像生成方法、装置及终端
WO2020238515A1 (fr) Procédé et appareil de mise en correspondance d'images, dispositif, support, et produit- programme
US10032091B2 (en) Spatial organization of images based on emotion face clouds
TW202121233A (zh) 影像處理方法、處理器、電子設備、儲存媒介
CN108961267B (zh) 图片处理方法、图片处理装置及终端设备
CN110751218B (zh) 图像分类方法、图像分类装置及终端设备
WO2016112797A1 (fr) Procédé et dispositif pour déterminer des informations d'affichage d'image
WO2021129466A1 (fr) Procédé de détection de filigrane, dispositif, terminal et support de stockage
WO2023000895A1 (fr) Procédé et appareil de conversion de style d'image, dispositif électronique et support de stockage
CN111047509A (zh) 一种图像特效处理方法、装置及终端
US11804043B2 (en) Detecting objects in a video using attention models
WO2022206729A1 (fr) Procédé et appareil pour sélectionner une couverture de vidéo, dispositif informatique, et support de stockage
WO2023024413A1 (fr) Procédé et appareil de correspondance d'informations, dispositif informatique et support de stockage lisible
CN111598149B (zh) 一种基于注意力机制的回环检测方法
CN111553838A (zh) 模型参数的更新方法、装置、设备及存储介质
CN110163095B (zh) 回环检测方法、回环检测装置及终端设备
CN115937546A (zh) 图像匹配、三维图像重建方法、装置、电子设备以及介质
CN108629767B (zh) 一种场景检测的方法、装置及移动终端
US11934958B2 (en) Compressing generative adversarial neural networks
Han Texture image compression algorithm based on self-organizing neural network
WO2021051562A1 (fr) Procédé et appareil de positionnement de point de caractéristique faciale, dispositif informatique et support de stockage

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21756441

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21756441

Country of ref document: EP

Kind code of ref document: A1