Method for extracting characteristic of natural image based on dispersion-constrained non-negative sparse coding

Abstract

The invention discloses a method for extracting the characteristic of a natural image based on dispersion-constrained non-negative sparse coding, which comprises the following steps of: partitioning an image into blocks, reducing dimensions by means of 2D-PCA, non-negative processing image data, initializing a wavelet characteristic base based on 2D-Gabor, defining the specific value between intra-class dispersion and extra-class dispersion of a sparsity coefficient, training a DCB-NNSC characteristic base, and image identifying based on the DCB-NNSC characteristic base, etc. The method has the advantages of not only being capable of imitating the receptive field characteristic of a V1 region nerve cell of a human eye primary vision system to effectively extract the local characteristic of the image; but also being capable of extracting the characteristic of the image with clearer directionality and edge characteristic compared with a standard non-negative sparse coding arithmetic; leading the intra-class data of the characteristic coefficient to be more closely polymerized together to increase an extra-class distance as much as possible with the least constraint of specific valuebetween the intra-class dispersion and the extra-class dispersion of the sparsity coefficient; and being capable of improving the identification performance in the image identification.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, in particular to an image feature extraction method based on Dispersion Constraint Based Non-negative Sparse Coding (DCB-NNSC) in digital image processing technology. Background technique [0002] With the advent of the information society, the information people obtain is no longer limited to numbers, symbols, texts and other information, but more and more image information. Because most of the image information has a very high dimension, or the number of obtained images is huge, this brings great inconvenience to the storage and processing of image information. For real-time systems, it is undoubtedly difficult to achieve. And in most cases, object classification and recognition cannot be done directly in these measurement spaces. On the one hand, this is because the dimensionality of the measurement space is very high, which is not suitable for the design of classifi...

AI Technical Summary

Problems solved by technology

Although the SC algorithm and the non-negative sparse coding algorithm can simulate the human visual physiological model and can extract image features, they do not consider the information of the image category, and the extracted features cannot be better used for pattern recognition.

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