An efficient Harris hawks-inspired image segmentation method

Rodrí­guez-Esparza, Erick; Zanella-Calzada, Laura A.; Oliva, Diego; Heidari, Ali Asghar; Zaldívar, Daniel; Pérez‐Cisneros, Marco; Foong, Loke Kok

doi:10.1016/j.eswa.2020.113428

Cited by 142 publications

(47 citation statements)

References 73 publications

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“…Recently, Eric et al [ 40 ] introduced an efficient swarm optimizer called harris hawks optimizer (HHO) for solving multilevel thresholding based on minimum cross-entropy. The authors treat the standard benchmark of images and medical mammograms.…”

Section: Related Workmentioning

confidence: 99%

“…In this side, several algorithms are introduced for multilevel thresholding images including Harris hawk's optimizer, grey wolf optimizer, ant colony optimization, artificial bee colony ant lion optimizer, whale optimization algorithm, salp swarm algorithm. Recently, Eric et al [40] introduced an efficient swarm optimizer called harris hawks optimizer (HHO) for solving multilevel thresholding based on minimum cross-entropy. The authors treat the standard benchmark of images and medical mammograms.…”

Section: Related Workmentioning

confidence: 99%

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Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Elaziz

Neggaz

Moghdani

et al. 2021

Multimed Tools Appl

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Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Elaziz

Neggaz

Moghdani

et al. 2021

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…D. Oliva combined the thresholding techniques and the evolutionary Bayesian network algorithm to generate the accurate class even in complex condition [61]. E. R. Esparza represented an efficient harris hawks method used into the image segmentation so as to produce the efficient and reliable results [62].…”

Section: Literature Reviewmentioning

confidence: 99%

A Logistic Chaotic Barnacles Mating Optimizer With Masi Entropy for Color Image Multilevel Thresholding Segmentation

Zheng

Sun

et al. 2020

IEEE Access

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Barnacles mating optimizer (BMO) is an evolutionary algorithm that simulates the mating and reproductive behavior of barnacle population. In this paper, an improved Barnacles mating optimizer based on logistic model and chaotic map (LCBMO) was proposed to produce the high-quality optimal result. Firstly, the logistic model is introduced into the native BMO to realize the automatic conversion parameters. This strategy maintains a proper relationship between exploitation and exploration. Then, the chaotic map is integrated to enhance the exploitation capability of the algorithm. After that, six variants based on LCBMO are compared to find the best algorithm on benchmark functions. Moreover, to the knowledge of the authors, there is no previous study on this algorithm for multilevel color image segmentation. LCBMO takes Masi entropy as the objective function to find the optimal threshold. By comparing different thresholds, different types of images, different optimization algorithms, and different objective functions, our proposed technique is reliable and promising in solving color image multilevel thresholding segmentation. Wilcoxon rank-sum test and Friedman test also prove that the simulation results are statistically significant. INDEX TERMS Barnacles mating optimizer, logistic model, chaotic map, masi entropy, multilevel thresholding, color image segmentation.

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“…Oliva et al proposed a BNMTH method for multilevel image segmentation, and the results indicated that the BNMTH method had better segmentation performance [35]. Rodrí guez-Esparza et al proposed the Harris hawks optimization algorithm based on the minimum crossentropy to solve the image segmentation problem, which was found to be an effective and feasible method [36]. Alwerfali et al combined the salp swarm algorithm with fuzzy entropy for multilevel image thresholding, and the results indicated that the segmentation performance of the proposed algorithm is significantly better than that of other algorithms [37].…”

Section: Introductionmentioning

confidence: 99%

Kapur’s Entropy for Underwater Multilevel Thresholding Image Segmentation Based on Whale Optimization Algorithm

et al. 2021

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Multilevel thresholding is an effective and indispensable technology for image segmentation that has attracted extensive attention in recent years. However, the multilevel thresholding method has some disadvantages, such as a large computational complexity and low segmentation accuracy. Therefore, this paper proposes a whale optimization algorithm (WOA) based on Kapur's entropy method to solve the image segmentation problem. The WOA can effectively balance exploration and exploitation to avoid falling into premature convergence and obtain the global optimal solution. To verify the segmentation performance of the WOA, a series of experiments on underwater images from the experimental pool of Harbin Engineering University are conducted, and the segmentation results are compared with those of the BA, the FPA, MFO, the MSA, PSO and WWO by maximizing the fitness value of Kapur's entropy method. The fitness value, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), execution time and Wilcoxon's rank-sum test are used to evaluate the overall performance of each algorithm. The experimental results reveal that the WOA is superior to the other comparison algorithms and has a higher segmentation accuracy, better segmentation effect and stronger robustness. In addition, the feasibility and efficiency of the WOA are verified.

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An efficient Harris hawks-inspired image segmentation method

Cited by 142 publications

References 73 publications

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

A Logistic Chaotic Barnacles Mating Optimizer With Masi Entropy for Color Image Multilevel Thresholding Segmentation

Kapur’s Entropy for Underwater Multilevel Thresholding Image Segmentation Based on Whale Optimization Algorithm

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