Method of image segmentation based on immune spectrum clustering

Abstract

The invention discloses an image segmentation method based on immunity spectrum clustering, which includes: 1. extracting texture characteristic of the input image, representing each pixel point in the image with an eigenvector to obtain a characteristic set; 2. mappings the characteristic set to a linear measure space by spectrum clustering to a mapping set; 3. dividing the category number according to the given image, accidentally selecting the corresponding number of data from the mapping set as the initial clustering center, executing cloning, variation, selection and judgement in sequence, to find out a optimum clustering center with the same category number with the initial clustering center; 4. dividing all pixel points of the characteristic set to an optimum clustering center nearest to the pixel points, and giving each pixel point a category mark according to the category of optimum clustering center where the pixel point locates to complete the image segmentation. Compared with the prior technology, the invention has advantages of insensitivity to initialization, quick convergence to global optimum and high specification accuracy, which can be used in the image segmentation of SAR image processing and computer visual sense field.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image segmentation method, which can be used for image segmentation in the fields of SAR image processing and computer vision. Background technique [0002] Image segmentation is one of the basic and key technologies in image processing and computer vision. It refers to dividing an image into regions with different characteristics. A region in an image refers to an interconnected pixel with consistent "meaningful" attributes. gather. In many machine vision and image processing algorithms, a simple assumption is made on image features: the intensity of local areas of the image is uniform, and different areas have different intensities. However, there are also many images of actual objects that do not satisfy this assumption, such as wood surfaces, hair, weaves, grass, and sandy beaches. "texture", where distinct regions in an image are identified based on texture rathe...

AI Technical Summary

Problems solved by technology

However, this classic spectral clustering algorithm uses k-means clustering in the mapping domain. The k-means itself has the disadvantages of being sensitive to initialization, easy to fall into local optimum, and difficult to implement. Therefore, it is used for image segmentation. poor result

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