Computer Science ›› 2014, Vol. 41 ›› Issue (8): 306-310.doi: 10.11896/j.issn.1002-137X.2014.08.065

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Multilevel Thresholding Image Segmentation Based on Improved Artificial Fish Swarm Algorithm

CUI Li-qun,SONG Xiao,LI Hong-xu and ZHANG Ming-jie   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In order to realize the effective image segmentation,this paper proposed a new multilevel thresholding algorithm based on improved fish swarm algorithm for image segmentation.It introduces the idea of domain search for basic AFSA to make further improvements,and then realizes global optimization for the maximum entropy function.The improved algorithm could automatically adjust the parameters of fish swarm algorithm according to the individual fitness and the degree of dispersion of the population,while it ensures the diversity of the population and accelerates the convergence speed at the same time,and finally gets the vintage thresholds for image segmentation.It overcomes the shortcomings of basic AFSA including poor convergence,easy to fall into local optimum and other issues.Experimental results show that the proposed algorithm can achieve a more stable,fast and accurate image segmentation.

Key words: Artificial fish swarm algorithm(AFSA),Maximum entropy,Multilevel thresholding,Image segmentation

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