Incomplete multi-view image segmentation method, system and device and storage medium

Abstract

The invention discloses an incomplete multi-view image segmentation method, system and device, and a storage medium. The method comprises the following steps: obtaining an incomplete multi-view feature matrix of a to-be-segmented image; defining an anchor point matrix and an anchor point diagram, and minimizing the error between the representation of the anchor point matrix and the anchor point diagram and the incomplete multi-view feature matrix by using an adaptive norm; in the anchor point diagram self-learning framework, endowing the anchor point diagram self-learning framework with different learnable weights in different visual angles; carrying out unilateral orthogonal non-negative matrix factorization on the anchor point diagram, integrating the anchor point diagram into a target framework, and carrying out iterative convergence on the target framework to obtain an updated coefficient matrix; and extracting a segmentation result of the to-be-segmented image from the coefficient matrix. And the efficiency and the performance of a large-scale multi-view image segmentation task are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of image processing and relates to an incomplete multi-view image segmentation method, system, device and storage medium. Background Art

[0002] In recent years, image segmentation algorithms based on multi-view clustering have attracted widespread attention due to their ability to integrate richer image information, and have been successfully applied to different fields such as remote sensing analysis, autonomous driving, and medical diagnosis. However, the image acquisition process is often affected by uncontrollable factors, resulting in incomplete multi-view features, which makes it difficult to apply existing segmentation methods based on complete multi-view clustering; at the same time, with the rapid increase in the number of images to be processed in the real world, multi-view data gradually presents large-scale and high-dimensional characteristics. Traditional multi-view clustering methods are difficult to effectively process these large-scale high-dimensional multi-view data due to high time and space overhead. How to improve the performance and efficiency of large-scale incomplete multi-view image segmentation methods while dealing with incompleteness has become an important research issue in the field of information technology. In this invention, we mainly focus on the incomplete multi-view data of images, and based on the incomplete multi-view clustering theory, propose an efficient incomplete multi-view image segmentation method and system.

[0003] At present, the segmentation methods for large-scale incomplete multi-view image data can be divided into two categories: the first category is based on the anchor graph method, that is, after obtaining the multi-view features from the image, a small number of anchor points are first generated, and an anchor graph between the original data and the anchor points is constructed, and then a spectral clustering operation is performed. There are two different ways to generate anchor points in this type of method. One is to generate predefined anchor points, that is, using Methods such as the mean first generate anchor points, and then build an anchor graph based on this. However, this method often causes a mismatch between the two stages of anchor point generation and anchor graph construction, resulting in a decrease in segmentation accuracy. The second method is to synchronously iteratively update the anchor points and anchor graphs through self-learning, which solves the two-stage mismatch problem while improving the effectiveness of segmentation. The second type of method for large-scale incomplete data is based on the accelerated matrix decomposition method. For the original data operation, it is decomposed into the product of a small-scale coefficient matrix and a base matrix, and then the obtained coefficient matrix is ​​subjected to The segmentation results are obtained by mean clustering. Compared with traditional multi-view image segmentation methods, the computational complexity of these methods is significantly reduced, which improves the efficiency of multi-view image segmentation and enables it to be used for large-scale tasks.

[0004] However, the efficiency of the above two methods still needs to be improved in large-scale image segmentation tasks. For the first type of anchor graph-based methods, constructing anchor graphs instead of traditional full sample graphs reduces the computational complexity, but the time and space overhead of spectral clustering of anchor graphs is still high; for the second type of matrix decomposition-based methods, when faced with high-dimensional data, directly decomposing the original data becomes time-consuming, that is, the segmentation efficiency is restricted by large-scale matrix multiplication. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide an incomplete multi-view image segmentation method, system, device and storage medium, which greatly improves the efficiency and performance of large-scale multi-view image segmentation tasks.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: An incomplete multi-view image segmentation method includes the following processes: S01, obtaining an incomplete multi-view feature matrix of the image to be segmented; S02, define the anchor matrix and anchor graph, use the adaptive norm to minimize the error between the representation of the anchor matrix and anchor graph and the incomplete multi-view feature matrix, and obtain the anchor graph self-learning framework; S03, assigning weights of different perspectives to the anchor graph self-learning framework to obtain a weighted anchor graph self-learning framework; S04, performing unilateral orthogonal non-negative matrix factorization on the anchor graph to obtain an anchor graph decomposition framework, integrating the weighted anchor graph self-learning framework and the anchor graph self-learning framework into a target framework, iteratively converging the target framework, and obtaining an updated coefficient matrix; S05, extracting the segmentation result of the image to be segmented from the coefficient matrix.

[0007] Preferably, in S1, the image to be segmented is in the form of an incomplete multi-view image data set, a missing sample indication matrix is ​​defined, the incomplete multi-view image data set is multiplied by the missing indication matrix, and the missing rows or columns caused by the incomplete multi-view image data set are removed to obtain an ordered incomplete multi-view feature matrix.

[0008] Preferably, in S2, an anchor point matrix independent of each perspective and an anchor point map matrix consistent with all perspectives are defined, an anchor map consistent with all perspectives is defined, and under the constraint of an adaptive norm, each perspective-independent anchor point and an anchor point map consistent with the perspective are obtained by self-learning, and the anchor point matrix, the anchor point map and the missing indicator matrix are multiplied to obtain the representation of the anchor point matrix and the anchor map.

[0009] Preferably, in S3, self-learning weights and power parameters are added to the anchor graph self-learning framework of different perspectives.

[0010] Preferably, in S4, a unilateral orthogonal non-negative matrix factorization is adopted, using Norm constraint decomposition,reconstructs the anchor graph.

[0011] Preferably, in S4, each variable in the target frame is updated one by one through an iterative optimization algorithm until the value change of the target frame is less than a threshold and converges to obtain an updated coefficient matrix.

[0012] Preferably, in S5, Mean clustering,extracts segmentation results for multi-view image datasets from the coefficient matrix.

[0013] An incomplete multi-view image segmentation system, comprising: A feature acquisition module, used to acquire an incomplete multi-view feature matrix of the image to be segmented; Anchor graph learning module, which is used to define anchor matrix and anchor graph, and use adaptive norm to minimize the error between the representation of anchor matrix and anchor graph and the incomplete multi-view feature matrix, so as to obtain anchor graph self-learning framework; The weight assignment module is used to assign weights to different perspectives in the anchor graph self-learning framework to obtain a weighted anchor graph self-learning framework; The target framework module is used to perform unilateral orthogonal non-negative matrix factorization on the anchor graph to obtain the anchor graph decomposition framework, integrate the weighted anchor graph self-learning framework and the anchor graph self-learning framework into the target framework, iterate and converge the target framework to obtain the updated coefficient matrix; The segmentation result acquisition module is used to extract the segmentation result of the image to be segmented from the coefficient matrix.

[0014] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the incomplete multi-view image segmentation method when executing the computer program.

[0015] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the incomplete multi-view image segmentation method are implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention integrates anchor point learning based on adaptive norm and anchor point graph decomposition into a unified clustering framework, which can effectively improve the clustering efficiency and endow the model with the characteristics of global anchor point adaptive adjustment and overall robustness with the help of adaptive norm; the anchor point graph is decomposed to obtain a coefficient matrix using unilateral orthogonal non-negative matrix decomposition, which can effectively reduce the size of the matrix and further improve the framework performance. It can be robustly applied to actual noisy image segmentation tasks, greatly improving the efficiency and performance of large-scale multi-view image segmentation tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the incomplete multi-view image segmentation method of the present invention; Figure 2a-2c This is a graph showing how various segmentation indicators of the present invention vary with parameters.

[0018] Figure 3a-3c is a convergence graph of iterative optimization of three incomplete multi-view image data sets to be clustered according to the present invention; Figure 4a-4c A visualization diagram of the segmentation results of the present invention with increasing numbers of iterations; Figure 5a-Figure 5i This is a comparison chart of the accuracy of the method of the present invention and other image segmentation methods; Figure 6a-6c This is a comparison chart of the efficiency of the method of the present invention and other image segmentation methods. DETAILED DESCRIPTION

[0019] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0020] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0021] like Figure 1 As shown in FIG. 1 , the incomplete multi-view image segmentation method based on global adaptive anchor graph decomposition according to the present invention includes the following processes: Step S01: Establish a machine learning model to obtain incomplete multi-view features of an image to be segmented, where the image to be segmented is in the form of an incomplete multi-view image dataset.

[0022] Feeding machine learning models with incomplete multi-view image datasets In order to deal with the missing part of the data matrix caused by incomplete features, for the input incomplete multi-view image dataset ,definition is a given indicator vector containing the index of the existing samples of the vth perspective. On this basis, the missing sample indicator matrix is ​​defined ,in represents the number of samples, It is The number of instances that are not missing from each perspective, The specific definition is as follows:

[0023] Incomplete multi-view image dataset and missing indicator matrix Multiply to get , we can get the processed ordered incomplete multi-view feature matrix, which contains incomplete multi-view features. By introducing the missing indicator matrix To remove the missing rows or columns caused by incomplete multi-view image datasets.

[0024] Step S02: define the anchor matrix and anchor graph, multiply the anchor matrix, anchor graph and missing indicator matrix to obtain the representation of the anchor matrix and anchor graph, use the adaptive norm to minimize the error between the representation of the anchor matrix and anchor graph and the incomplete multi-view feature matrix, and under the constraint of the adaptive norm, obtain the independent anchor point of each view and the anchor graph with consistent view by self-learning, so as to improve the robustness of the machine learning model.

[0025] In some embodiments, each view-independent anchor matrix and an anchor matrix consistent across all views are defined, where the anchor matrix is ​​defined as: ,in Indicates The number of dimensions of the perspective data, Expressed as the number of anchor points; define an anchor point map with consistent viewing angles , the anchor matrix, anchor graph and missing indicator matrix are multiplied to obtain the representation of the anchor matrix and anchor graph. The anchor graph self-learning framework with adaptive norm constraint can be expressed as:

[0026]

[0027] in represents the adaptive norm, is the parameter of the adaptive norm, Represents a vector whose elements are all 1.

[0028] Step S03: assign weights to the anchor graph self-learning framework at different perspectives, and assign different importance to anchor learning at different perspectives.

[0029] Add self-learning weights and power parameters to anchor graphs of different perspectives. The self-learning framework of anchor graphs with weights is expressed as follows:

[0030]

[0031] in , indicating the The anchor graph learning weights of each perspective are used to measure the different weights of different perspectives in the consistent anchor graph, and to give different importance to the anchor learning of different perspectives. is the power parameter of the weight, fixed to 2.

[0032] Step S04: performing unilateral orthogonal non-negative matrix factorization on the anchor graph to obtain a coefficient matrix for characterizing the sample category structure.

[0033] In some embodiments, a one-sided orthogonal non-negative matrix factorization is employed, using Norm Constraint Anchor Graph Matrix As well as the decomposed matrix, the anchor graph is reconstructed to reduce the number of matrix multiplication operations. The anchor graph decomposition framework is expressed as:

[0034]

[0035] in express norm, is the coefficient matrix, is an orthogonal matrix.

[0036] The weighted anchor graph self-learning framework and the anchor graph decomposition framework are integrated into the target framework of the machine learning model, that is, the incomplete multi-view image segmentation framework. The complete target equation can be written as:

[0037]

[0038] in is a hyperparameter that balances the self-learning anchor term and the anchor graph factorization term. The relationship between the changes of the three parameters and the changes of image segmentation indicators and segmentation time is as follows: Figure 2a-2c As shown in Figure 2, the line graph shows the impact of the three hyperparameters in this method on the effectiveness and efficiency of clustering. The hyperparameters are the number of anchor points, ( Figure 2a ), balance parameter ( Figure 2b ) and the adaptive norm parameter ( Figure 2c ). Clustering effectiveness is represented by ACC, NMI and Purity (%), and efficiency is represented by time (s).

[0039] Each variable is updated one by one through the iterative optimization algorithm until the value change of the target equation is less than the threshold value and the model converges to obtain the updated coefficient matrix .

[0040] Step S05: Use Mean clustering,extracts segmentation results for incomplete multi-view image datasets from the coefficient matrix.

[0041] In some embodiments, the coefficient matrix that can characterize the sample category structure obtained by decomposing the full-view consistent anchor point graph is ,use The mean clustering method extracts the discrete categories corresponding to the samples and obtains the image segmentation results.

[0042] like Figure 3a-3c As shown in Figure 3, the three incomplete multi-view image datasets to be clustered have all converged effectively after iterative optimization, proving that good convergence results are achieved for different incomplete multi-view image datasets to be clustered. Figure 4a-4c As shown in the figure, as the number of iterations increases, the segmentation effect becomes better and better.

[0043] The embodiments of the present invention can effectively improve the efficiency of incomplete multi-view image segmentation and maintain high segmentation effectiveness, and improve the multi-view image segmentation results under the premise of having good robustness. Compared with other incomplete multi-view segmentation methods that can be used for image segmentation in the related art, the image segmentation effectiveness of the embodiments of the present invention is higher, such as Figure 5a-Figure 5i shown. Figure 5a-Figure 5i The segmentation performance comparison between the embodiment of the present invention and other advanced methods on real multi-view image datasets of different types and sizes (conventional datasets and large-scale datasets) is shown. The method indicators that cannot complete segmentation within the specified time or have insufficient memory are represented as 0 in the bar graph. The effectiveness comparison between the present method and four comparison methods on three different missing ratios corresponding to five image datasets is shown through 3D bar graphs. Figure 5a-5c , Figure 5d-5f and Figure 5g-Figure 5i The three groups represent 10%, 30%, and 50% sample missing rates, respectively. Figure 5a-5c , Figure 5d-5f and Figure 5g-Figure 5i The three groups presented ACC, NMI and Purity indicators in turn. Each image showed HCP-IMSC, MIMB, SEC-IMVC and HCLS as comparison methods, and MSRC, Yale, Mnist4, STL-10 and Animal as image datasets. Among the different indicators, the segmentation effect of the embodiment of the present invention was the highest.

[0044] The embodiment of the present invention can significantly improve the efficiency of incomplete multi-view image segmentation. Compared with other incomplete multi-view segmentation methods that can be used for image segmentation in the related art, the image segmentation efficiency of the embodiment of the present invention is significantly improved. Figure 6a-6c shown. Figure 6a-6c The segmentation runtime comparison between the embodiments of the present invention and other advanced methods on real multi-view image datasets of different types and sizes (conventional datasets and large-scale datasets) is shown. Figure 6a-6c The efficiency comparison of this method and four comparison methods on the MSRC, Yale and Caltech101-7 image datasets is shown in turn. Each image shows the running time (s) of each method under the conditions of 10%, 30%, and 50% sample missing rate of the dataset. The comparison methods are: HCP-IMSC, MIMB, SEC-IMVC and HCLS. Through the comparison of running time, the segmentation efficiency of the embodiment of the present invention is the highest.

[0045] It can be seen that the present invention is an efficient and high-precision incomplete multi-view image segmentation method based on global adaptive anchor graph decomposition. By performing anchor graph decomposition on the anchor graph learned under the constraint of the adaptive norm, the data scale is reduced, the computational complexity is reduced, and the clustering efficiency is greatly improved. At the same time, a new self-learning anchor graph framework based on adaptive norm constraints is proposed, which utilizes the robustness brought by the adaptive norm and the global adaptive adjustment capability to improve the effectiveness of clustering.

[0046] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0047] In another embodiment of the present invention, an incomplete multi-view image segmentation system is provided. The incomplete multi-view image segmentation system can be used to implement the above-mentioned incomplete multi-view image segmentation method. Specifically, the incomplete multi-view image segmentation system includes a feature acquisition module, an anchor graph learning module, a weight assignment module, a target framework module and a segmentation result acquisition module.

[0048] Among them, the feature acquisition module is used to obtain an incomplete multi-view feature matrix of the image to be segmented.

[0049] The anchor graph learning module is used to define the anchor matrix and anchor graph, and uses the adaptive norm to minimize the error between the representation of the anchor matrix and anchor graph and the incomplete multi-view feature matrix to obtain the anchor graph self-learning framework.

[0050] The weight assignment module is used to assign weights of different perspective features in the anchor graph in the anchor graph self-learning framework, so as to obtain a weighted anchor graph self-learning framework.

[0051] The target framework module is used to perform unilateral orthogonal non-negative matrix factorization on the anchor graph to obtain the anchor graph decomposition framework, integrate the weighted anchor graph self-learning framework and the anchor graph self-learning framework into the target framework, iterate and converge the target framework to obtain the updated coefficient matrix.

[0052] The segmentation result acquisition module is used to extract the segmentation result of the image to be segmented from the coefficient matrix.

[0053] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the incomplete multi-view image segmentation method, including: S01, obtaining an incomplete multi-view feature matrix of the image to be segmented; S02, defining an anchor matrix and an anchor map, and using an adaptive norm to convert the anchor points into The error between the representation of the matrix and the anchor graph and the incomplete multi-view feature matrix is ​​minimized to obtain the anchor graph self-learning framework; S03, in the anchor graph self-learning framework, the weights of different view features in the anchor graph are assigned to obtain the weighted anchor graph self-learning framework; S04, the anchor graph is factorized by a unilateral orthogonal non-negative matrix to obtain the anchor graph decomposition framework, the weighted anchor graph self-learning framework and the anchor graph self-learning framework are integrated into the target framework, the target framework is iteratively converged to obtain the updated coefficient matrix; S05, the segmentation result of the image to be segmented is extracted from the coefficient matrix.

[0054] In another embodiment, the present invention further provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0055] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the incomplete multi-view image segmentation method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and the following steps are executed: S01, obtaining an incomplete multi-view feature matrix of the image to be segmented; S02, defining an anchor matrix and an anchor graph, and using an adaptive norm to minimize the error between the representation of the anchor matrix and the anchor graph and the incomplete multi-view feature matrix to obtain an anchor graph self-learning framework; S03, in the anchor graph self-learning framework, assigning weights of different view features in the anchor graph to obtain a weighted anchor graph self-learning framework; S04, performing unilateral orthogonal non-negative matrix factorization on the anchor graph to obtain an anchor graph decomposition framework, integrating the weighted anchor graph self-learning framework and the anchor graph self-learning framework into a target framework, iteratively converging the target framework to obtain an updated coefficient matrix; S05, extracting the segmentation result of the image to be segmented from the coefficient matrix.

[0056] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0057] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0058] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0060] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0061] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0062] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0063] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this patent should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a waiver of such subject matter, nor should it be considered that the applicant has not considered such subject matter to be part of the disclosed inventive subject matter.

Claims

1. An incomplete multi-view image segmentation method, characterized in that: The process includes: S01, obtaining an incomplete multi-view feature matrix of the image to be segmented; S02, define the anchor matrix and anchor graph, use the adaptive norm to minimize the error between the representation of the anchor matrix and anchor graph and the incomplete multi-view feature matrix, and obtain the anchor graph self-learning framework; S03, assigns different learnable weights to the anchor graph self-learning framework in different perspectives; S04, performing unilateral orthogonal non-negative matrix factorization on the anchor graph to obtain an anchor graph decomposition framework, integrating the weighted anchor graph self-learning framework and the anchor graph decomposition framework into a target framework, iteratively updating the target framework until convergence, and obtaining an updated coefficient matrix; S05, extracting the segmentation result of the image to be segmented from the coefficient matrix.

2. The incomplete multi-view image segmentation method according to claim 1, characterized in that: In S1, the image to be segmented is in the form of an incomplete multi-view image data set. A missing sample indication matrix is ​​defined, the incomplete multi-view image data set is multiplied by the missing indication matrix, and the missing rows or columns caused by the incomplete multi-view image data set are removed to obtain an ordered incomplete multi-view feature matrix.

3. The incomplete multi-view image segmentation method according to claim 1, characterized in that: In S2, each view-independent anchor point matrix and all view-consistent anchor point graph matrices are defined, and all view-consistent anchor point graphs are defined. Under the constraint of the adaptive norm, each view-independent anchor point and the view-consistent anchor point graph are obtained by self-learning. The anchor point matrix, anchor graph and missing indicator matrix are multiplied to obtain the representation of the anchor point matrix and anchor graph.

4. The incomplete multi-view image segmentation method according to claim 1, characterized in that: In S3, self-learning weights and power parameters are added to the anchor graph self-learning framework.

5. The incomplete multi-view image segmentation method according to claim 1, characterized in that: In S4, a one-sided orthogonal non-negative matrix factorization is used, using Norm-constrained decomposition,performs low-rank reconstruction on the anchor graph.

6. The incomplete multi-view image segmentation method according to claim 1, characterized in that: In S4, the learnable variables in the target frame are updated one by one through an iterative optimization algorithm until the value change of the target frame is less than the threshold and converges to obtain an updated coefficient matrix.

7. The incomplete multi-view image segmentation method according to claim 1, characterized in that: In S5, Mean clustering,extracts segmentation results for multi-view image datasets from the coefficient matrix.

8. An incomplete multi-view image segmentation system, characterized in that: include: A feature acquisition module, used to acquire an incomplete multi-view feature matrix of the image to be segmented; Anchor graph learning module, which is used to define anchor matrix and anchor graph, and use adaptive norm to minimize the error between the representation of anchor matrix and anchor graph and the incomplete multi-view feature matrix, so as to obtain anchor graph self-learning framework; The weight assignment module is used to assign weights to the anchor graph self-learning framework at different perspectives to obtain a weighted anchor graph self-learning framework; The target framework module is used to perform unilateral orthogonal non-negative matrix factorization on the anchor graph to obtain the anchor graph decomposition framework, integrate the weighted anchor graph self-learning framework and the anchor graph self-learning framework into the target framework, iterate and converge the target framework to obtain the updated coefficient matrix; The segmentation result acquisition module is used to extract the segmentation result of the image to be segmented from the coefficient matrix.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the incomplete multi-view image segmentation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the incomplete multi-view image segmentation method according to any one of claims 1 to 7 are implemented.

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