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Multilevel Image Segmentation Based on an Improved Firefly Algorithm

机译：基于改进萤火虫算法的多级图像分割

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摘要

Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA) is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy) are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.
机译：多级图像分割是耗时的并且涉及大量的计算。萤火虫算法已被应用于提高多级图像分割的效率。但是，在某些情况下，萤火虫算法很容易陷入局部最优状态。本文提出了一种改进的萤火虫算法（IFA）来搜索多级阈值。在IFA中，为了帮助萤火虫摆脱局部最优并加速收敛，提出了两种策略（即具有柯西突变的多样性增强策略和邻域策略），并根据不同的停滞站来自适应地选择。将提出的IFA与三种基准最优算法进行比较，即达尔文粒子群优化，混合差分进化优化和萤火虫算法。实验结果表明，与其他三种方法相比，该方法可以有效地对多级图像进行分割，并获得更好的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第2期|1578056.1-1578056.12|共12页
  • 作者

    Chen Kai; Zhou Yifan; Zhang Zhisheng; Dai Min; Chao Yuan; Shi Jinfei;

  • 作者单位

    Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China;

    Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China;

    Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China;

    Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China;

    Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China;

    Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China;

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