Image segmentation method based on differential immune clone clustering

Abstract

The invention discloses an image segmentation method based on differential immune clone clustering, belonging to the field of image processing and aiming at solving the problems that the existing clustering technology has slow convergence rate, poor stability and is easy to sink in local extreme values. The realization steps thereof are as follows: 1) extracting gray-level co-occurrence matrix and wavelet transform characteristics from an image to be segmented; 2) carrying out watershed pre-segmentation on the image to be segmented, getting the mean value of pixel characteristics belonging tothe same block to obtain clustering data; 3) carrying out initialization and individual encoding on species group 1; 4) calculating the fitness value of individuals in species group 1 to obtain the antibody in species group 2 to update an elite species group; 5) respectively designing different manipulators for the species group 1 and the species group 2, carrying out differential variation, orthogonal recombination, binominal intersection and selection operation in sequence on the species group 1, and carrying out proportional cloning, hyper-mutation and cloning selection operation on the species group 2 in sequence; and 6) outputting image segmentation results. The invention has the advantages of high convergence speed, high stability, good consistency of segmentation results regions and complete reserved information, can effectively segment texture images and SAR images, and can be applied to target recognition of SAR images.

Description

technical field [0001] The invention belongs to the field of image processing, relates to a method for segmenting texture images and SAR images, and can be applied to target recognition. Background technique [0002] Image segmentation is a basic technique in image processing. It is to divide the image space into some regions according to certain characteristics of the image (such as gray level, frequency spectrum, texture, etc.). Image segmentation technology is widely used in practice. The extraction and measurement of image objects are inseparable from image segmentation. The accuracy of segmentation directly affects the effectiveness of subsequent tasks, so it is of great significance. Currently commonly used image segmentation techniques mainly use threshold segmentation techniques, edge segmentation techniques, and region growth segmentation techniques. [0003] Clustering, that is, unsupervised classification, is an important data analysis method and an important bra...

AI Technical Summary

Problems solved by technology

Although the above clustering techniques can overcome the shortcomings of traditional clustering techniques such as sensitivity to initial values ​​and noise, they use a single population evolution method and traditional update population operations, which can easily reduce population diversity and thus fall into local extremum, seriously affecting Image Segmentation Results

Although there are some improved technologies, such as changing the way of cross-mutation, increasing the population size, etc., none of them can fundamentally solve the above problems

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