Image Segmentation Method Based on Multi-objective Evolutionary Algorithm

Abstract

The invention discloses an image segmentation method based on a multi-objective evolutionary algorithm. The image segmentation method mainly solves the problems that an existing image segmentation method is poor in robustness, and local optimum and prematurity phenomena are easily caused. The method includes the implementation methods that firstly, an image to be segmented is input; secondly, texture features of the image to be segmented are extracted according to the type of the image; thirdly, the image to be segmented is preliminarily segmented, and preliminarily-segmented object data are acquired and initialized so that populations are acquired; fourthly, fitness degrees of individuals in the populations are calculated; fifthly, non-dominated populations are acquired according to the fitness degrees of the individuals, and active populations are selected from the non-dominated populations; sixthly, whether an end condition is met or not is judged, if yes, an optimal individual is selected from the active populations acquired in the fifth step, the optimal individual is marked and a finally-segmented image is generated, or else the active population is evolved, the fourth step is executed again, and the fitness degrees of the individuals in the populations are recalculated. The image segmentation method has the advantages of being high in robustness and stability, and can be suitable for segmentation of a synthetic aperture radar image, a natural image and a texture image.

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to an image segmentation method. The invention can be used for segmenting synthetic aperture radar SAR images, texture images and natural images. Background technique [0002] Image segmentation is one of the key technologies in computer vision and pattern recognition. In recent years, many scholars have applied intelligent computing technology to the field of image segmentation, which mainly includes neural networks, genetic algorithms, swarm intelligence algorithms, and artificial immune system frameworks. Evolutionary multi-objective optimization is an important research direction in the field of evolution, and multi-objective genetic algorithm based on the concept of pareto optimal solution is a research hotspot of genetic algorithm at present. The so-called segmentation is to divide the image into several parts, each part represents a different feature i...

AI Technical Summary

Problems solved by technology

This method can ensure the integrity of spatial information and reduce noise, but the disadvantage of this method is that it relies too much on the initial value, is sensitive to the selection of membership degree, and is prone to premature phenomenon. The robustness of this method is poor.

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