Texture image segmentation method combined with sparse neighbor propagation and rapid spectral clustering

Abstract

The invention discloses a texture image segmentation method combined with sparse neighbor propagation and rapid spectral clustering, and the method mainly solves the problems that in an existing texture image segmentation method, the segmentation accuracy is low and the computation complexity is high. The method comprises the steps that 1, an image to be segmented is input; 2, parameters are set; 3, a grayscale-probability square matrix is generated; 4, the number of data points is counted; 5, a sparse similarity matrix is built; 6, data representative points are selected; 7, clustering is performed on the data points; 8, the image to be segmented is marked; 9, a segmented image is output. Compared with some existing texture image segmentation technologies, the texture image segmentation method combined with sparse neighbor propagation and rapid spectral clustering can better maintain the region consistency of the texture image, the texture image with high segmentation accuracy and good segmentation stability can be obtained, and the computation complexity is low.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a texture image segmentation method combined with sparse nearest neighbor propagation and fast spectral clustering in the technical field of image segmentation. The invention can be used for segmentation of various texture images to achieve the purpose of identifying and analyzing targets. Background technique [0002] Image segmentation is one of the basic problems in image processing, and it is the basis for realizing target recognition on images. Among them, texture image segmentation is an important branch of digital image processing research and the basis of many image analysis and machine vision applications. [0003] In image segmentation methods, feature-based texture image segmentation is usually completed by two steps of feature extraction and image segmentation. The purpose of texture image segmentation is to divide the image into several disjoint regi...

AI Technical Summary

Problems solved by technology

Although this method can better maintain the regional consistency of the texture image, improve the segmentation accuracy, and make the segmentation result more in line with human vision, there are still shortcomings: because the immune cloning algorithm belongs to the evolutionary algorithm, when using the immune cloning algorithm for texture During image segmentation, the stability of the segmentation results is poor, and effective image segmentation results cannot be obtained

Although the image segmentation obtained by this patented technology is more reliable than the existing technology, there are still shortcomings: this patented technology only extracts the local feature information of the image to represent the overall situation, the extraction effect is random, and it is easy to lose important information. resulting in poor segmentation

In addition, the method of reducing the amount of data by principal component analysis is easy to fall into local optimum, and the edge information is easy to lose, which limits the accuracy of segmentation results

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