Image segmentation method based on iteration self-organization and multi-agent inheritance clustering algorithm

Abstract

The invention discloses an image segmentation method based on an iteration self-organization and multi-agent inheritance clustering algorithm. The method mainly solves the problems that a segmentation result depends on initial parameters excessively, and the phenomenon of local optimum occurs easily in the prior art. The method comprises the segmentation steps that 1) gray information of an image to be segmented is extracted; 2) the algorithm thought of the iteration self-organization algorithm ISODATA is used for the image to be segmented to obtain the optimal clustering number; 3) according to the optimal clustering number, a multi-agent algorithm frame is utilized for clustering the image to be segmented to obtain an optimal clustering label; 4) according to the optimal clustering label, image pixels of the image to be segmented are classified to achieve image segmentation. According to the method, the clustering number does not need to be determined definitely, the convergence effect is good, the global optimum value can be obtained easily, the quality of image segmentation can be improved, the stability of the segmentation result is enhanced, and the method can be used for extraction and identification of image targets and other follow-up processing.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, relates to an image segmentation method, and can be used in the field of pattern recognition and computer vision. Background technique [0002] Image segmentation is a key technology in image processing, which is widely used in image processing research. For example, target recognition and target measurement are all based on image segmentation, and the results of image segmentation directly affect the follow-up tasks. Therefore, the research on image segmentation is of great significance. Image segmentation is a special image processing technology, which is essentially a process of classifying according to the image pixel attributes, namely grayscale, texture, and color. The commonly used methods in the existing image segmentation methods include image segmentation methods based on clustering and image segmentation methods based on edge extraction. Among them, the application o...

AI Technical Summary

Problems solved by technology

The advantage of this algorithm is that it is simple and easy to operate, but at the same time it also brings a lot of inconvenience, such as: depending on the number of clustering types initially set, easy to fall into local optimum, unsatisfactory clustering results, etc.

In order to solve this kind of problem, researchers have made many attempts. Some people use the combination of genetic algorithm GA and clustering algorithm, and get satisfactory results. However, due to the limitations of the global evolution mechanism of traditional genetic algorithm, the combined clustering method It still has the disadvantages of relying on the initial set category of clustering and easily falling into local optimal values, which leads to a decrease in the quality of image segmentation results and a decrease in the stability of segmentation results, which is not conducive to subsequent image analysis and processing

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