Combined method and system for extracting and classifying features of images

Abstract

The invention discloses a combined method and system for extracting and classifying features of images. The combined method comprises the following steps: firstly, constructing a neighbor graph according to the similarity of training samples and calculating and reconstructing a coefficient matrix; introducing nonlinear manifold learning with minimum neighbor reconstructing error measured on the basis of nuclear norm measurement, performing low-dimension manifold feature learning on a training image sample, thereby acquiring a linear projection matrix capable of extracting the low-dimension manifold feature of the sample; utilizing the low-dimension manifold feature of the training sample to minimize L2,1-norm regularization classifying error, completing robust sparse classifier learning and outputting the optimal classifier, thereby extracting and classifying the features of the tested sample. Compared with the prior art, the method has the advantage that the combination of nuclear norm measurement and L2,1-norm regularization is adopted for effectively increasing the descriptiveness of feature extraction and classification accuracy.

Description

technical field [0001] The present invention relates to the technical fields of computer vision and image recognition, and more specifically, relates to an image feature extraction and classification joint method and system. Background technique [0002] In a large number of practical applications, real data can be described by high-dimensional attributes or features. However, the dimension of the original feature may be very large, or the sample is in a very high-dimensional space, and the high-dimensional data can be transformed into a low-dimensional space through the method of feature mapping or feature transformation. Extracting the most effective features for classification from high-dimensional features has always been one of the very important and difficult research topics in the research fields of computer vision and image recognition. [0003] In order to extract better features, it is often necessary to consider the similarity or locality between data in the proc...

AI Technical Summary

Problems solved by technology

But like LLE, NPE also uses the traditional Frobenius norm distance to measure the neighbor reconstruction error, so the common disadvantage of the two is that they cannot accurately measure the neighbor reconstruction error, and the Frobenius norm is very sensitive to noise

In addition, both are dimensionality reduction methods, which do not consider classification errors, that is, they cannot ensure that the extracted neighbor-preserving features are optimal for classification, and the effectiveness of feature extraction and classification accuracy are low.

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