Image classification method based on combination of SRC and MFA

Abstract

The invention discloses an image classification method based on combination of SRC and MFA, which is mainly used for solving the problem that the image classification result is not ideal because only the reconstruction relationship or local discriminant structure is considered and the sample information cannot be accurately described in the conventional feature extraction method. The method comprises the following steps: 1. inputting a training sample and a test sample, constructing the same kind and different kinds of sample matrixes, and initializing a projection matrix; 2. projecting the training sample, respectively taking the same kind and different kinds of samples as dictionaries, solving sparse representation coefficients of the samples, and constructing the same kind and different kinds of sparse weight matrixes; 3. constructing an objective function to solve a novel projection matrix; 4. iterating the steps 2 and 3 until the cycle index is larger than the set initial value, outputting the final projection matrix, and projecting the test samples; and 5. classifying the test samples by utilizing a sparse representation classifier. According to the method disclosed by the invention, the accuracy of image classification is enhanced, and the method can be used for discriminating identity of characters or searching objects during image shooting in a police work system.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image classification method, which can be used for identification of persons in police systems or search for objects in fields such as video shooting. Background technique [0002] Image classification is one of the hot and challenging research directions in machine learning, pattern recognition and computer vision. Image classification technology has been widely used in intelligent transportation, public security, biomedicine, e-commerce, remote sensing technology, military and multimedia network communication and other fields because of its advantages of simplicity, efficiency, safety and low cost. Images are often affected by imaging factors such as viewing angle, illumination, and occlusion, which brings great challenges to image classification. Image classification is a pattern recognition problem in high-dimensional space. Therefore, when recognizi...

AI Technical Summary

Problems solved by technology

[0006] One is that the overall classification results are not ideal because only the reconstruction of the data is considered and the local discriminant information of the data is ignored.

[0007] The second is that because only the reconstruction residual is considered, the discriminant structure cannot be better described, especially in the case of a large number of categories, the SRC-DP method cannot be effectively identified

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