Method for segmenting gray scale image based on multi-objective particle swarm optimization algorithm

Abstract

The invention discloses a method for segmenting a gray scale image based on the multi-objective particle swarm optimization algorithm, relates to the technical field of image processing, and mainly solves a problem that a traditional method is single in evaluation index, has high possibility of leading poor consistency of regions, and is disordered in edge. The method comprises the following steps: (1) extracting characteristics of a gray scale image to be segmented, namely selecting a gray scale of the image as data for a cluster to be clustered; (2) setting an operating parameter and initializing a particle swarm; (3) performing multi-objective clustering on the data combining the multi-objective particle swarm optimization algorithm to obtain a solution set approximate to Pareto; (4) selecting a solution with the largest PBM index from the solution set obtained in the step (3) as the optimal solution, namely a clustering result; (5) allocating a clustering mark to each pixel of the image to be segmented according to the clustering result obtained in the step (4) to obtain a segmenting result. The method has the advantages that the consistency of regions is good according to the segmenting result, complete information can be reserved, and the computing speed is quick, and can be used for image target identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an image segmentation method, in particular to a grayscale image segmentation method, and can be applied to target recognition. Background technique [0002] With the development of computer technology, images are widely used in various industries. Grayscale image segmentation is the basis of obtaining information in the form of images, which is a hot research topic and one of the important contents of image processing technology application. [0003] Image segmentation is an important step in image processing. The task of image segmentation is to divide the input image into some independent regions, so that the same region has the same attributes, and different regions have different attributes. For the problem of image segmentation, researchers have proposed many methods, but in view of the characteristics of many types of images, large amount of data, and many changes, so...

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Problems solved by technology

[0005] Traditional clustering methods often have the following two disadvantages when performing grayscale image segmentation: (1) The evaluation index is single, and only one objective function is used, that is, the sum of the distances between the points in each class and its cluster center is the smallest , to perform clustering, resulting in inaccurate classification results; (2) Sensitive to initialization, if some solutions with relatively small fitness are randomly generated during initialization, the probability of mis-segmentation will be relatively high

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