Image characteristics extracting method based on combination of SRC-DP and LDA

Abstract

The invention discloses an image characteristics extracting method based on combination of SRC-DP and LDA. The image characteristics extracting method mainly solves the problems that an existing characteristics extracting method only takes reconstitution relation or distinguish relation into consideration, cannot accurately describe sample information and is not ideal in image classifying results. The image characteristics extracting method comprises the step 1 of inputting training sample, calculating out an intra-class dispersion matrix and a between-class dispersion matrix of the sample and initializing a projection matrix, the step 2 of projecting the training sample and sequentially solving sparse representation coefficients of the projection matrix, the step 3 of respectively calculating out an intra-class reconstitution dispersion matrix and a between-class reconstitution dispersion matrix of projection sample, the step 4 of constructing a target function and solving a new projection matrix, and the step 5 of carrying out iteration on the step 2 to the step 4 until the cycle index is larger than a given initial value and outputting a final projection matrix. The image characteristics extracting method enhances image classifying accuracy, improves the classification recognizing rate and can be applied to distinguishing character identities in a police system or searching articles in image shooting.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a feature extraction method in image recognition, which can be used for identification of persons in police systems or search for objects in fields such as image shooting. Background technique [0002] Image recognition is one of the hot and challenging research directions in machine learning, pattern recognition and computer vision. Image recognition technology has been widely used in intelligent transportation, public security, biomedicine, e-commerce, remote sensing technology, military and multimedia network communications and other fields due to 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 recognition belongs to the problem of pattern recognition in high-dimensional space. Therefore, when r...

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Problems solved by technology

[0004] One is that only the local reconstruction of the data is considered, and the global discriminant information of the data is ignored, which makes the overall classification result unsatisfactory.

[0005] 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

For example, for the c classification problem, it can find a c-1 projection direction, thereby compressing the dimension to c-1. Therefore, although this method has outstanding data compression capabilities, it can effectively reduce the amount of data for subsequent processing, and can effectively However, when using this method for image recognition, only the overall discriminant information of the sample is considered, and the sample is not analyzed from the perspective of reconstruction, which leads to unsatisfactory classification results.

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