Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization
Multi-threshold segmentation of color images is a critical component of modern image processing. However, as the number of thresholds increases, traditional multi-threshold image segmentation methods face challenges such as low accuracy and slow convergence speed. To optimize threshold selection in...
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MDPI AG
2025-04-01
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| author | Yunlong Hu Liangkuan Zhu Hongyang Zhao |
| author_facet | Yunlong Hu Liangkuan Zhu Hongyang Zhao |
| author_sort | Yunlong Hu |
| collection | DOAJ |
| description | Multi-threshold segmentation of color images is a critical component of modern image processing. However, as the number of thresholds increases, traditional multi-threshold image segmentation methods face challenges such as low accuracy and slow convergence speed. To optimize threshold selection in color image segmentation, this paper proposes a multi-strategy improved Electric Eel Foraging Optimization (MIEEFO). The proposed algorithm integrates Differential Evolution and Quasi-Opposition-Based Learning strategies into the Electric Eel Foraging Optimization, enhancing its search capability, accelerating convergence, and preventing the population from falling into local optima. To further boost the algorithm’s search performance, a Cauchy mutation strategy is applied to mutate the best individual, improving convergence speed. To evaluate the segmentation performance of the proposed MIEEFO, 15 benchmark functions are used, and comparisons are made with seven other algorithms. Experimental results show that the MIEEFO algorithm outperforms other algorithms in at least 75% of cases and exhibits similar performance in up to 25% of cases. To further explore its application potential, a multi-level Kapur entropy-based MIEEFO threshold segmentation method is proposed and applied to different types of benchmark images and forest fire images. Experimental results indicate that the improved MIEEFO achieves higher segmentation quality and more accurate thresholds, providing a more effective method for color image segmentation. |
| format | Article |
| id | doaj-art-b65c47e60ea343ada3c8cf038959e2d5 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-b65c47e60ea343ada3c8cf038959e2d52025-08-20T02:09:11ZengMDPI AGMathematics2227-73902025-04-01137121210.3390/math13071212Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging OptimizationYunlong Hu0Liangkuan Zhu1Hongyang Zhao2College of Computer and Control Engineering, Northeast Foresty University, 26 Hexing Road, Harbin 15004, ChinaCollege of Computer and Control Engineering, Northeast Foresty University, 26 Hexing Road, Harbin 15004, ChinaCollege of Computer and Control Engineering, Northeast Foresty University, 26 Hexing Road, Harbin 15004, ChinaMulti-threshold segmentation of color images is a critical component of modern image processing. However, as the number of thresholds increases, traditional multi-threshold image segmentation methods face challenges such as low accuracy and slow convergence speed. To optimize threshold selection in color image segmentation, this paper proposes a multi-strategy improved Electric Eel Foraging Optimization (MIEEFO). The proposed algorithm integrates Differential Evolution and Quasi-Opposition-Based Learning strategies into the Electric Eel Foraging Optimization, enhancing its search capability, accelerating convergence, and preventing the population from falling into local optima. To further boost the algorithm’s search performance, a Cauchy mutation strategy is applied to mutate the best individual, improving convergence speed. To evaluate the segmentation performance of the proposed MIEEFO, 15 benchmark functions are used, and comparisons are made with seven other algorithms. Experimental results show that the MIEEFO algorithm outperforms other algorithms in at least 75% of cases and exhibits similar performance in up to 25% of cases. To further explore its application potential, a multi-level Kapur entropy-based MIEEFO threshold segmentation method is proposed and applied to different types of benchmark images and forest fire images. Experimental results indicate that the improved MIEEFO achieves higher segmentation quality and more accurate thresholds, providing a more effective method for color image segmentation.https://www.mdpi.com/2227-7390/13/7/1212electric eel foraging optimizationswarm intelligencemulti-threshold image segmentationKapur’s entropy |
| spellingShingle | Yunlong Hu Liangkuan Zhu Hongyang Zhao Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization Mathematics electric eel foraging optimization swarm intelligence multi-threshold image segmentation Kapur’s entropy |
| title | Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization |
| title_full | Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization |
| title_fullStr | Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization |
| title_full_unstemmed | Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization |
| title_short | Multistage Threshold Segmentation Method Based on Improved Electric Eel Foraging Optimization |
| title_sort | multistage threshold segmentation method based on improved electric eel foraging optimization |
| topic | electric eel foraging optimization swarm intelligence multi-threshold image segmentation Kapur’s entropy |
| url | https://www.mdpi.com/2227-7390/13/7/1212 |
| work_keys_str_mv | AT yunlonghu multistagethresholdsegmentationmethodbasedonimprovedelectriceelforagingoptimization AT liangkuanzhu multistagethresholdsegmentationmethodbasedonimprovedelectriceelforagingoptimization AT hongyangzhao multistagethresholdsegmentationmethodbasedonimprovedelectriceelforagingoptimization |