Method for extracting features of two-dimensional image

Abstract

The invention discloses a method for extracting the features of a two-dimensional image. The method comprises the following steps of S1, carrying out Gabor filter difference decomposition on texture images, and obtaining a binary image set; S2, extracting textural features from decomposed binary images by using statistical operators; S3, carrying out dimensionality reduction on the extracted textural features by using a principal component analysis method, and carrying out texture classification and retrieval on feature vectors existing after dimensionality reduction is carried out. The method for extracting the features of the two-dimensional image has the advantages that texture elements do not need to be defined, the computation complexity is appropriate, and the five newly proposed statistical operators more comprehensively describe the arrangement rules of texture elements and elements existing after the decomposition; in a recognition process, because the principal component analysis algorithm is introduced, the computation speed of the method is increased, and the method for extracting the features of the two-dimensional image is suitable for real-time retrieval and classification of the texture images.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a feature extraction method of a two-dimensional image. Background technique [0002] Texture is an important visual attribute of an image, which is easy to identify but difficult to define. After decades of research, researchers have proposed hundreds of texture description methods. These methods can be roughly divided into statistical methods, structural methods, model methods and signal processing methods. Among them, the structural method is most in line with the cognitive characteristics of human beings. This kind of method believes that the texture image is composed of different texture primitives according to different arrangement rules. For example, in the beach texture, each grain of sand is the texture primitive of the texture, and the random distribution rules among the sands are the arrangement rules of the primitives. [0003] Structural methods were prop...

AI Technical Summary

Problems solved by technology

For the statistical geometric feature method, the global threshold is used to decompose the image, so that the method does not have illumination invariance

Secondly, for the decomposed binary image, only the geometric information of the individual binary patches is extracted, and the distribution information between the patches is not described at all, so that the final description features are incomplete and the recognition ability is limited.

For the statistical multi-scale patch feature method, although the two-dimensional decomposition of the spatial scale and the gray scale improves the description ability of the feature, the dimension of the extracted feature is also greatly increased, which increases the computational complexity.

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