A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation
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 differ...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/1/8 |
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| author | Seyed Jalaleddin Mousavirad Davood Zabihzadeh Diego Oliva Marco Perez-Cisneros Gerald Schaefer |
| author_facet | Seyed Jalaleddin Mousavirad Davood Zabihzadeh Diego Oliva Marco Perez-Cisneros Gerald Schaefer |
| author_sort | Seyed Jalaleddin Mousavirad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:32:05Z |
| format | Article |
| id | doaj.art-e38fd5c8be3948bf8ddbb4774a01c505 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T01:32:05Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e38fd5c8be3948bf8ddbb4774a01c5052023-11-23T13:40:22ZengMDPI AGEntropy1099-43002021-12-01241810.3390/e24010008A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image SegmentationSeyed Jalaleddin Mousavirad0Davood Zabihzadeh1Diego Oliva2Marco Perez-Cisneros3Gerald Schaefer4Computer Engineering Department, Hakim Sabzevari University, Sabzevar 96179-76487, IranComputer Engineering Department, Hakim Sabzevari University, Sabzevar 96179-76487, IranDepartamento de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara 44430, MexicoDepartamento de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara 44430, MexicoDepartment of Computer Science, Loughborough University, Loughborough LE11 3TT, UKMasi 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.https://www.mdpi.com/1099-4300/24/1/8image segmentationmulti-level image thresholdingoptimisationdifferential evolutionclustering |
| spellingShingle | Seyed Jalaleddin Mousavirad Davood Zabihzadeh Diego Oliva Marco Perez-Cisneros Gerald Schaefer A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation Entropy image segmentation multi-level image thresholding optimisation differential evolution clustering |
| title | A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation |
| title_full | A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation |
| title_fullStr | A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation |
| title_full_unstemmed | A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation |
| title_short | A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation |
| title_sort | grouping differential evolution algorithm boosted by attraction and repulsion strategies for masi entropy based multi level image segmentation |
| topic | image segmentation multi-level image thresholding optimisation differential evolution clustering |
| url | https://www.mdpi.com/1099-4300/24/1/8 |
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