Improved non-convex robust principal component analysis method

A principal component analysis, non-convex technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve problems such as hindering development, loss of information in principal component analysis methods, and difficulty in solving problems, and achieves improved effects and richness. The effect of structured information

CN111428795APending Publication Date: 2020-07-17NANJING COLLEGE OF INFORMATION TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-07-17

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Abstract

The invention discloses an improved non-convex robust principal component analysis method. The method comprises the steps of converting a to-be-processed video into a two-dimensional matrix D with thesize of m rows and n columns; inputting the two-dimensional matrix D into a pre-constructed model of an improved non-convex robust principal component analysis method, wherein the output is a low-rank matrix B corresponding to a video background and a sparse matrix F corresponding to a video foreground, and the model of the improved non-convex robust principal component analysis method adopts a generalized non-convex kernel norm as a rank function of the model and adopts a structured sparse norm as a 10 norm in the model. The method has the advantages that the rank function in the traditionalrobust principal component analysis method can be better approximated, and the effect of the robust principal component analysis method in foreground and background separation of the video is improved. According to the method, a structured sparse norm is introduced, a structured sparse model is established for the foreground of the video, the structured information of the model is greatly enriched, and the effect of separating the foreground and the background of the video influenced by factors such as illumination and fluctuation by a robust principal component analysis method is improved.
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Description

technical field

[0001] The invention relates to an improved non-convex robust principal component analysis method, which belongs to the technical field of multimedia processing. Background technique

[0002] At present, the robust principal component analysis method is widely used in traffic control, social security and signal processing as the main method of video foreground and background separation. main research object. The traditional video foreground and background separation methods are mainly based on pixel-level processing methods, which have many defects and often ignore the structured information between pixels in the video, and the effect is not obvious; with the development of video processing technology, the main The component analysis method came into being. This method mainly uses singular value decomposition to reduce the dimensionality of video-based multidimensional data. , but there are many defects that cannot be ignored, and also seriously hinder the ...

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Embodiment Construction

[0041] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the following The described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0042] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and through specific implementation methods.

[0043] like figure 1As shown in the figure, the figure is a structural block diagram of an improved non-convex robust principal component analysis method....