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

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Yi Wang ◽  
Kangshun Li
Keyword(s):  
Heuristic Search ◽  
Firefly Algorithm ◽  
Color Image ◽  
Search Algorithm ◽  
Color Images ◽  
Multilevel Thresholding ◽  
Threshold Values ◽  
Heuristic Search Algorithm ◽  
Kapur’S Entropy ◽  
Fuzzy Adaptive

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.

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

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Heuristic Search ◽  
Firefly Algorithm ◽  
Color Image ◽  
Search Algorithm ◽  
Color Images ◽  
Multilevel Thresholding ◽  
Threshold Values ◽  
Heuristic Search Algorithm ◽  
Kapur’S Entropy ◽  
Fuzzy Adaptive

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.

A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding

2021 ◽  
pp. 2250012
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Space ◽  
Energy Curve ◽  
Local Optimum ◽  
Multilevel Thresholding ◽  
Curve Method ◽  
Heuristic Search Methods ◽  
Multi Level ◽  
Computationally Expensive

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.

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