Robust image classification method and device based on low-rank two-dimensional local identification image embedding

Abstract

The invention discloses a robust image classification method and device based on low-rank two-dimensional local identification image embedding, and the method comprises the steps: constructing an image library unit which is used for obtaining a standard image library, and constructing a new to-be-classified standard image library; the first calculation unit is used for calculating the difference J(P) between the intra-class divergence matrix Sw and the inter-class divergence matrix Sb of the new standard image to be classified; the first image processing unit is used for carrying out low-rankmatrix decomposition on the acquired image X to obtain a low-rank matrix A and a sparse matrix E; the second calculation unit is used for obtaining a final target function according to the combination of results of the first calculation unit and the first image processing unit; the feature matrix calculation unit is used for obtaining a feature matrix Y; and the nearest neighbor classifier unit is used for classifying the images by utilizing a nearest neighbor classifier and outputting a classification result of the images. According to the invention, the technical problems of low classification precision, noise points and singular points in the existing image classification based on the 2DLPP learning model are solved, and the identification precision is improved.

Description

technical field [0001] The invention relates to a robust image classification method and device based on embedding of low-rank two-dimensional local discriminant graphs. Background technique [0002] In recent decades, in order to solve the "curse of dimensionality" problem in machine learning, image processing, computer vision and pattern recognition, many projection-based linear feature extraction techniques have been developed, including principal component analysis (PCA) and linear discriminant analysis (LDA) and its extended versions, such as two-dimensional PCA (2DPCA), two-dimensional LDA (2DLDA), etc. However, linear techniques may fail to discover the underlying nonlinear data structures. In practical applications, nonlinear data include non-Gaussian and manifold-valued data. Therefore, representative nonlinear manifold learning techniques are proposed to reveal hidden semantics. , while maintaining the geometry of the manifold. Among manifold learning theories an...

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Problems solved by technology

[0007] In view of the above problems, the present invention provides a robust image classification method and device based on low-rank two-dimensional local discriminative graph embedding. Solve the technical problems of low classification accuracy, noise points and singular points in the existing image classification based on 2DLPP learning model, not only solve the noise of the sample, but also consider the category of the sample

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