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

Pub Date : 2021-10-01 DOI:10.4018/IJCINI.20211001.OA44
Yi Wang, Kangshun Li
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引用次数: 2

Abstract

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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基于模糊熵的多级彩色图像阈值模糊自适应萤火虫算法
多层阈值图像分割具有广泛的应用前景,是近年来研究的热点问题。元启发式搜索算法在求解多级阈值方面具有独特的优势。本文提出了一种模糊自适应萤火虫算法(FaFA),以模糊卡普尔熵为目标函数,求解彩色图像的最优多级阈值问题。在FaFA中,设计了一个模糊逻辑控制器来调节控制参数。实验共使用了6张卫星遥感彩色图像。将FaFA的性能与FA、BWO、SSA、NaFA和ODFA进行比较。在实验中进行了一些测量指标。实验结果表明,该算法明显优于其他五种算法。
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