CN112541509A - Image processing method fusing sparsity and low rank - Google Patents
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Abstract
The invention discloses an image processing method fusing sparsity and low rank, which relates to the technical field of image processing and has the technical scheme that: the method comprises the following steps: s1, establishing a robust domain adaptive image processing retrieval and management system; s2, detecting image processing based on the multi-source field of sparse and low-rank representation by using the intrinsic robustness of sparse and low-rank representation; s3, detecting image processing by using the large-scale data set; s4, embedding the multisource adaptive sparse and low-rank subspace into an image for processing and detecting; the method can realize robust effective detection of field adaptive image processing by utilizing multi-source Web image resources in the face of complex image processing and application environments, and can overcome the problem of robust effectiveness of the prior field adaptive learning method in image processing application.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an image processing method fusing sparseness and low rank.
Background
With the widespread establishment and application of Web-based open image resource libraries (such as Youke, YouTube and the like) at home and abroad, the continuous cheapness of digital storage media and the convenience of personal handheld Video image devices, a large amount of image (Consumer Video) resources are accumulated on the Web and in the personal storage devices of users, the phenomena of data flooding and knowledge poverty gradually appear, and a plurality of uncontrolled harmful (such as yellow, building lost, reaction and the like) consumed image resources also fill the Web space, so that the healthy use environment of the Web users is seriously damaged. Therefore, how to effectively index and reasonably manage these consumption image resources so as to obtain the beneficial image resources required by the user from them is a very significant practical requirement facing the current green image processing application.
Currently, although some beneficial advances have been made in user image concept recognition research, user image events, especially complex events, are still in the preliminary stage of detection research, and most of them are limited by the recognition and detection of abnormal events or pattern repeat events of images; in addition, conventional image processing methods need to use a large amount of training data with event labels to learn a robust classifier, and in the face of a large amount of unlabeled user image resources, these methods face the problem of poor learning performance due to limited or outdated training data in specific applications. Although manually labeling new training data can partially alleviate the problem, it will consume a lot of manpower and material resources, and also waste labeled resources;
therefore, the image processing method fusing the sparsity and the low rank is provided to solve the problem of robust effective detection of the field adaptive image processing by using the multi-source Web image resources in the face of complex image processing and application environments.
Disclosure of Invention
The invention aims to provide an image processing method fusing sparsity and low rank, which can realize robust effective detection of field adaptive image processing by using multisource Web image resources in the face of complex image processing and application environments, and can overcome the problem of robustness effectiveness of the prior field adaptive learning method in image processing application.
The technical purpose of the invention is realized by the following technical scheme: the method comprises the following steps:
s1, establishing a robust domain adaptive image processing retrieval and management system;
s2, detecting image processing based on the multi-source field of sparse and low-rank representation by using the intrinsic robustness of sparse and low-rank representation;
s3, detecting image processing by using the large-scale data set;
and S4, detecting the multi-source adaptive sparse and low-rank subspace embedding image processing.
The invention is further configured to: in step S2, the specific steps include:
s21, establishing a robust multi-source or (and) multi-core field adaptive image processing technical framework;
s22, extracting image data set features fusing sparse and low-rank coding and a maximum correlation criterion by using the existing interval optimized Relief feature weighting technology;
s23, analyzing the defects of the existing domain data overall distribution mean value and divergence consistency measurement criterion on the domain image data distribution distance measurement with manifold structure, based on manifold learning thought, providing a distribution distance measurement new criterion of distribution mean value in the domain image data class and Laplacian divergence consistency, and theoretically analyzing the internal relationship between the new and old criteria;
s24, representing image data of each field by adopting the existing graph regularization sparse and low-rank representation technology, and reconstructing a field distribution mean value and divergence difference through new image data representation, or called the field distribution sparse and low-rank mean value and divergence difference; the method comprises the steps of introducing prior knowledge into an existing graph regularization sparse and low-rank representation model, based on the thought of a maximum interval criterion, providing a discriminant graph regularization sparse and low-rank representation method to represent field image data, and then constructing a field intra-class distribution mean value and Laplacian divergence difference thereof, or called the field intra-class sparse and low-rank mean value and Laplacian divergence difference thereof, through new image data representation;
s25, establishing a sparse and low-rank graph model, and processing the field of a single source image by using a sparse and low-rank representation technology;
s26, establishing a cross-domain sparse multi-core learning model, and processing key influences of kernel space learning on domain distribution distance measurement convergence and cross-domain image processing generalization performance and a control method thereof;
s27, constructing a multi-source image adaptation joint sparse and low-rank graph regularization image processing model, further obtaining a unified framework for single-source and multi-source image field adaptive image processing, or called a multi-sparse and low-rank graph regularization field adaptive image processing generalized framework model, theoretically analyzing a generalization error bound of the proposed model, and practically and fully checking the effectiveness of the proposed framework by combining various loss models;
s28, performing sparsification processing on the traditional multi-source combination, selecting a compact source field image set with the most expressive power from an available source field pool, selecting the multi-source image field, and then expanding the discrimination capacity of the source image field decision function through the nearest tag space embedding.
The invention is further configured to: in step S3, the specific steps include:
s31, establishing a large-scale cross-domain image concept recognition method model;
and S32, extracting the features of the image data by using a Relief feature extraction technology, realizing a feature extraction technology based on sparse and low-rank representation, and further improving the robustness and effectiveness of image processing.
The invention is further configured to: in step S4, the specific steps include:
s41, establishing an image processing model based on sparse and low-rank subspace embedding;
and S42, performing sparse and low-rank representation on the extracted image features of each field, and mapping the low-dimensional representation of the image features to a field adaptive common subspace respectively to realize effective detection of image processing.
The invention is further configured to: the cross-domainSparse multi-kernel learning model DsAnd DtRespectively referring to a source image field and a target image field data set, b ═ b1,b2,...,bM]Reconstructing a coefficient vector for the kernel sparsity; according to the sparse representation idea, i in constraint beta1Under the condition of norm minimization, M basic kernel functions are utilized to combine and express a final kernel function, then based on a robust domain distribution distance measurement criterion, a domain distribution distance measurement function based on multi-kernel learning is constructed and fused with a multi-kernel support vector machine model, and finally l at beta is obtained1And (3) a cross-domain sparse multi-kernel image processing model under the norm minimization constraint.
The invention is further configured to: the multi-source image adaptation combined sparse and low-rank graph regularization image processing model can be constructed in two ways, wherein one way is that an ultra-complete basis is directly formed by a source field data set and a target field data set, and the other way is that a new sparse and low-rank reconstruction basis is sought through optimization.
In conclusion, the invention has the following beneficial effects: the processing method overcomes the robustness effectiveness problem of the prior art adaptive learning method in image processing application, the image detection has better robustness, accuracy and high efficiency, the multi-source Web image resources can be utilized to realize the robust effective detection of the field adaptive image processing in the face of complex image processing and application environment, and the long-distance targets of public welfare machine learning and mode recognition research such as image processing, green image processing and the like of the applicant are met.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a block diagram of a robust multi-source or multi-core domain adaptive image processing technique in an embodiment of the invention;
FIG. 2 is a block diagram of a sparse and low rank graph model in an embodiment of the invention;
FIG. 3 is a framework diagram of a cross-domain sparse multi-kernel learning model in an embodiment of the present invention;
FIG. 4 is a block diagram of a robust multi-source or multi-core domain adaptive image processing technique in an embodiment of the invention;
FIG. 5 is a framework diagram of a large-scale cross-domain image concept recognition method model in an embodiment of the invention;
FIG. 6 is a block diagram of an image processing model based on sparse and low rank subspace embedding in an embodiment of the present invention;
FIG. 7 is a block diagram of a system for robust domain adaptive image processing retrieval and management in an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element.
It will be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, as used herein, refer to an orientation or positional relationship indicated in the drawings that is solely for the purpose of facilitating the description and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and is therefore not to be construed as limiting the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Examples
Referring to fig. 1-7, a method for processing an image with low rank and sparsity fused includes the following steps:
s1, please refer to fig. 7, establishing a robust domain adaptive image processing retrieval and management system;
s2, detecting image processing based on the multi-source field of sparse and low-rank representation by using the intrinsic robustness of sparse and low-rank representation;
s3, detecting image processing by using the large-scale data set;
and S4, detecting the multi-source adaptive sparse and low-rank subspace embedding image processing.
In this embodiment, in step S2, the specific steps include:
s21, please refer to FIG. 1, establishing a robust multi-source or/and multi-core domain adaptive image processing technical framework;
s22, extracting image data set features fusing sparse and low-rank coding and a maximum correlation criterion by using the existing interval optimized Relief feature weighting technology;
s23, analyzing the defects of the existing domain data overall distribution mean value and divergence consistency measurement criterion on the domain image data distribution distance measurement with manifold structure, based on manifold learning thought, providing a distribution distance measurement new criterion of distribution mean value in the domain image data class and Laplacian divergence consistency, and theoretically analyzing the internal relationship between the new and old criteria;
s24, representing image data of each field by adopting the existing graph regularization sparse and low-rank representation technology, and reconstructing a field distribution mean value and divergence difference through new image data representation, or called the field distribution sparse and low-rank mean value and divergence difference; the method comprises the steps of introducing prior knowledge into an existing graph regularization sparse and low-rank representation model, based on the thought of a maximum interval criterion, providing a discriminant graph regularization sparse and low-rank representation method to represent field image data, and then constructing a field intra-class distribution mean value and Laplacian divergence difference thereof, or called the field intra-class sparse and low-rank mean value and Laplacian divergence difference thereof, through new image data representation;
s25, please refer to FIG. 2, establishing a sparse and low-rank graph model, and processing a single source image field by using a sparse and low-rank representation technology;
s26, please refer to fig. 3, through establishing a cross-domain sparse multi-kernel learning model, and processing key influence of kernel space learning on domain distribution distance measurement convergence and cross-domain image processing generalization performance and its control method;
s27, please refer to FIG. 4, a multi-source image adaptation joint sparse and low-rank graph regularization image processing model is constructed, a unified framework for single-source and multi-source image field adaptive image processing, or a generalized framework model for multi-sparse and low-rank graph regularization field adaptive image processing, is further obtained, a generalization error bound of the proposed model is theoretically analyzed, and the effectiveness of the proposed framework is practically and fully tested by combining various loss models;
s28, performing sparsification processing on the traditional multi-source combination, selecting a compact source field image set with the most expressive power from an available source field pool, selecting the multi-source image field, and then expanding the discrimination capacity of the source image field decision function through the nearest tag space embedding.
In this embodiment, in step S3, the specific steps include:
s31, please refer to FIG. 5, establishing a large-scale cross-domain image concept recognition method model;
and S32, extracting the features of the image data by using a Relief feature extraction technology, realizing a feature extraction technology based on sparse and low-rank representation, and further improving the robustness and effectiveness of image processing.
In this embodiment, in step S4, the specific steps include:
s41, please refer to fig. 6, establishing an image processing model based on sparse and low-rank subspace embedding;
and S42, performing sparse and low-rank representation on the extracted image features of each field, and mapping the low-dimensional representation of the image features to a field adaptive common subspace respectively to realize effective detection of image processing.
In the present embodiment, please refer to fig. 3, D in the cross-domain sparse multi-kernel learning modelsAnd DtRespectively referring to a source image field and a target image field data set, b ═ b1,b2,...,bM]Reconstructing a coefficient vector for the kernel sparsity; according to the sparse representation idea, i in constraint beta1Under the condition of norm minimization, M basic kernel functions are utilized to combine and express a final kernel function, then based on a robust domain distribution distance measurement criterion, a domain distribution distance measurement function based on multi-kernel learning is constructed and fused with a multi-kernel support vector machine model, and finally l at beta is obtained1And (3) a cross-domain sparse multi-kernel image processing model under the norm minimization constraint.
In this embodiment, the multi-source image adaptation joint sparse and low-rank graph regularization image processing model may be constructed in two ways, one is to directly form an overcomplete basis by the source domain and target domain data sets, and the other is to seek a new sparse and low-rank reconstruction basis through optimization.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (6)
1. An image processing method fusing sparsity and low rank is characterized in that: the method comprises the following steps:
s1, establishing a robust domain adaptive image processing retrieval and management system;
s2, detecting image processing based on the multi-source field of sparse and low-rank representation by using the intrinsic robustness of sparse and low-rank representation;
s3, detecting image processing by using the large-scale data set;
and S4, detecting the multi-source adaptive sparse and low-rank subspace embedding image processing.
2. The sparse-and-low-rank-fused image processing method as claimed in claim 1, wherein: in step S2, the specific steps include:
s21, establishing a robust multi-source or multi-core field adaptive image processing technical framework;
s22, extracting image data set features fusing sparse and low-rank coding and a maximum correlation criterion by using the existing interval optimized Relief feature weighting technology;
s23, analyzing the defects of the existing domain data overall distribution mean value and divergence consistency measurement criterion on the domain image data distribution distance measurement with manifold structure, based on manifold learning thought, providing a distribution distance measurement new criterion of distribution mean value in the domain image data class and Laplacian divergence consistency, and theoretically analyzing the internal relationship between the new and old criteria;
s24, representing image data of each field by adopting the existing graph regularization sparse and low-rank representation technology, and reconstructing a field distribution mean value and divergence difference through new image data representation, or called the field distribution sparse and low-rank mean value and divergence difference; the method comprises the steps of introducing prior knowledge into an existing graph regularization sparse and low-rank representation model, based on the thought of a maximum interval criterion, providing a discriminant graph regularization sparse and low-rank representation method to represent field image data, and then constructing a field intra-class distribution mean value and Laplacian divergence difference thereof, or called the field intra-class sparse and low-rank mean value and Laplacian divergence difference thereof, through new image data representation;
s25, establishing a sparse and low-rank graph model, and processing the field of a single source image by using a sparse and low-rank representation technology;
s26, establishing a cross-domain sparse multi-core learning model, and processing key influences of kernel space learning on domain distribution distance measurement convergence and cross-domain image processing generalization performance and a control method thereof;
s27, constructing a multi-source image adaptation joint sparse and low-rank graph regularization image processing model, further obtaining a unified framework for single-source and multi-source image field adaptive image processing, or called a multi-sparse and low-rank graph regularization field adaptive image processing generalized framework model, theoretically analyzing a generalization error bound of the proposed model, and practically and fully checking the effectiveness of the proposed framework by combining various loss models;
s28, performing sparsification processing on the traditional multi-source combination, selecting a compact source field image set with the most expressive power from an available source field pool, selecting the multi-source image field, and then expanding the discrimination capacity of the source image field decision function through the nearest tag space embedding.
3. The sparse-and-low-rank-fused image processing method as claimed in claim 1, wherein: in step S3, the specific steps include:
s31, establishing a large-scale cross-domain image concept recognition method model;
and S32, extracting the features of the image data by using a Relief feature extraction technology, realizing a feature extraction technology based on sparse and low-rank representation, and further improving the robustness and effectiveness of image processing.
4. The sparse-and-low-rank-fused image processing method as claimed in claim 1, wherein: in step S4, the specific steps include:
s41, establishing an image processing model based on sparse and low-rank subspace embedding;
and S42, performing sparse and low-rank representation on the extracted image features of each field, and mapping the low-dimensional representation of the image features to a field adaptive common subspace respectively to realize effective detection of image processing.
5. The sparse-and-low-rank-fused image processing method as claimed in claim 2, wherein: d in the cross-domain sparse multi-kernel learning modelsAnd DtRespectively referring to a source image field and a target image field data set, b ═ b1,b2,...,bM]Reconstructing a coefficient vector for the kernel sparsity; according to the sparse representation idea, i in constraint beta1Under the condition of norm minimization, M basic kernel functions are utilized to combine and express a final kernel function, then based on a robust domain distribution distance measurement criterion, a domain distribution distance measurement function based on multi-kernel learning is constructed and fused with a multi-kernel support vector machine model, and finally l at beta is obtained1And (3) a cross-domain sparse multi-kernel image processing model under the norm minimization constraint.
6. The sparse-and-low-rank-fused image processing method as claimed in claim 2, wherein: the multi-source image adaptation combined sparse and low-rank graph regularization image processing model can be constructed in two ways, wherein one way is that an ultra-complete basis is directly formed by a source field data set and a target field data set, and the other way is that a new sparse and low-rank reconstruction basis is sought through optimization.
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| CN111860612A (en) * | 2020-06-29 | 2020-10-30 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Unsupervised hyperspectral image hidden low-rank projection learning feature extraction method |
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