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Article

A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation

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Computer Engineering Department, Hakim Sabzevari University, Sabzevar 96179-76487, Iran
2
Departamento de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara 44430, Mexico
3
Department of Computer Science, Loughborough University, Loughborough LE11 3TT, UK
*
Authors to whom correspondence should be addressed.
Academic Editor: Amelia Carolina Sparavigna
Entropy 2022, 24(1), 8; https://doi.org/10.3390/e24010008
Received: 21 November 2021 / Revised: 14 December 2021 / Accepted: 15 December 2021 / Published: 21 December 2021
(This article belongs to the Special Issue Entropy in Soft Computing and Machine Learning Algorithms)
Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm. View Full-Text
Keywords: image segmentation; multi-level image thresholding; optimisation; differential evolution; clustering image segmentation; multi-level image thresholding; optimisation; differential evolution; clustering
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MDPI and ACS Style

Mousavirad, S.J.; Zabihzadeh, D.; Oliva, D.; Perez-Cisneros, M.; Schaefer, G. A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation. Entropy 2022, 24, 8. https://doi.org/10.3390/e24010008

AMA Style

Mousavirad SJ, Zabihzadeh D, Oliva D, Perez-Cisneros M, Schaefer G. A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation. Entropy. 2022; 24(1):8. https://doi.org/10.3390/e24010008

Chicago/Turabian Style

Mousavirad, Seyed J., Davood Zabihzadeh, Diego Oliva, Marco Perez-Cisneros, and Gerald Schaefer. 2022. "A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation" Entropy 24, no. 1: 8. https://doi.org/10.3390/e24010008

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