Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm
Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms oft...
| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/9/3225 |
| _version_ | 1827717353096347648 |
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| author | Wei Liu Yongkun Huang Zhiwei Ye Wencheng Cai Shuai Yang Xiaochun Cheng Ibrahim Frank |
| author_facet | Wei Liu Yongkun Huang Zhiwei Ye Wencheng Cai Shuai Yang Xiaochun Cheng Ibrahim Frank |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively. |
| first_indexed | 2024-03-10T20:00:13Z |
| format | Article |
| id | doaj.art-533d91ab86a34c12a28f67fac4ef50c2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:00:13Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-533d91ab86a34c12a28f67fac4ef50c22023-11-19T23:35:52ZengMDPI AGApplied Sciences2076-34172020-05-01109322510.3390/app10093225Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics AlgorithmWei Liu0Yongkun Huang1Zhiwei Ye2Wencheng Cai3Shuai Yang4Xiaochun Cheng5Ibrahim Frank6School of Computer Science, Hubei University of Technology, Wuhan 430070, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430070, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430070, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430070, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430070, ChinaDepartment of Computer Science, Middlesex University, London NW4 4BT, UKSchool of Computer Science, Hubei University of Technology, Wuhan 430070, ChinaMulti-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.https://www.mdpi.com/2076-3417/10/9/3225image segmentationmulti-level thresholdingRenyi’s entropymeta-heuristics algorithm |
| spellingShingle | Wei Liu Yongkun Huang Zhiwei Ye Wencheng Cai Shuai Yang Xiaochun Cheng Ibrahim Frank Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm Applied Sciences image segmentation multi-level thresholding Renyi’s entropy meta-heuristics algorithm |
| title | Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm |
| title_full | Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm |
| title_fullStr | Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm |
| title_full_unstemmed | Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm |
| title_short | Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm |
| title_sort | renyi s entropy based multilevel thresholding using a novel meta heuristics algorithm |
| topic | image segmentation multi-level thresholding Renyi’s entropy meta-heuristics algorithm |
| url | https://www.mdpi.com/2076-3417/10/9/3225 |
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