An Image Classification Method Based on Sparse Representation Dictionary Learning

Abstract

The invention discloses an image classification method based on sparse representation dictionary learning, which can eliminate the correlation between different types of dictionaries to improve its discrimination performance, improve the representation ability of the dictionary and the robustness of the dictionary learning model. The method adopts a dictionary learning algorithm model based on block diagonal sparse representation,

Description

technical field [0001] The invention belongs to the technical field of image processing and image classification, and in particular relates to an image classification method based on sparse representation dictionary learning. Background technique [0002] In the past few years, sparse representations have achieved great success in many applications such as face recognition, image classification, and human action recognition. The core idea of ​​sparse representation is that most natural signals can be represented by a small number of atoms in an over-complete dictionary. In order to solve the problems encountered in practical applications, researchers have successively proposed many dictionary learning methods. Among them, a simple and direct method is to use the training samples themselves as dictionary atoms, such as the sparse representation classification (SRC) method. The success of this self-representation method is based on the subspace theory. The subspace theory a...

AI Technical Summary

Problems solved by technology

[0003] Traditional dictionary learning methods (such as MOD and K-SVD) are usually unsupervised, and the category characteristics of the data are not introduced in the dictionary learning process, which can enhance the discriminative ability of the dictionary in recognition problems

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