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

被引：1
作者：
Wang, Yi [1 ]
Li, Kangshun [2 ]
机构：
[1] Guangdong Univ Sci & Technol, Dongguan, Peoples R China
[2] South China Agr Univ, Guangzhou, Peoples R China
来源：
INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE | 2021年 / 15卷 / 04期
关键词：
Color Image Segmentation; Fuzzy Adaptive Firefly Algorithm; Fuzzy Kapur's Entropy; Multilevel Thresholding; MOTH-FLAME OPTIMIZATION; DIFFERENTIAL EVOLUTION; SEGMENTATION;
D O I：
10.4018/IJCINI.20211001.oa44
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multilevel thresholding image segmentation has been a hot issue of research in the last several years since it has a plenty of applications. 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.
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页数：20
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