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Multilevel Image Segmentation Based on an Improved Firefly Algorithm

Author

Listed:
  • Kai Chen
  • Yifan Zhou
  • Zhisheng Zhang
  • Min Dai
  • Yuan Chao
  • Jinfei Shi

Abstract

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.

Suggested Citation

  • Kai Chen & Yifan Zhou & Zhisheng Zhang & Min Dai & Yuan Chao & Jinfei Shi, 2016. "Multilevel Image Segmentation Based on an Improved Firefly Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-12, February.
    • Handle: RePEc:hin:jnlmpe:1578056
    DOI: 10.1155/2016/1578056
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    Cited by:

    1. Jin-Ling Bei & Ming-Xin Zhang & Ji-Quan Wang & Hao-Hao Song & Hong-Yu Zhang, 2023. "Improved Hybrid Firefly Algorithm with Probability Attraction Model," Mathematics, MDPI, vol. 11(2), pages 1-59, January.

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