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A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

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  • Yi Wang

    (Guangdong University of Science and Technology, Dongguan, China)

  • Kangshun Li

    (South China Agricultural University, Guangzhou, China)

Abstract

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.

Suggested Citation

  • Yi Wang & Kangshun Li, 2021. "A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(4), pages 1-20, October.
    • Handle: RePEc:igg:jcini0:v:15:y:2021:i:4:p:1-20
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