Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation
Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many r...
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MDPI AG
2022-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/7/1014 |
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| author | Qingxin Liu Ni Li Heming Jia Qi Qi Laith Abualigah |
| author_facet | Qingxin Liu Ni Li Heming Jia Qi Qi Laith Abualigah |
| author_sort | Qingxin Liu |
| collection | DOAJ |
| description | Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (<i>PSNR</i>), structure similarity (<i>SSIM</i>), and feature similarity (<i>FSIM</i>). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation. |
| first_indexed | 2024-03-09T11:39:24Z |
| format | Article |
| id | doaj.art-966ddd63b3e444fa9df4f96a73d6b862 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T11:39:24Z |
| publishDate | 2022-03-01 |
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| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-966ddd63b3e444fa9df4f96a73d6b8622023-11-30T23:35:54ZengMDPI AGMathematics2227-73902022-03-01107101410.3390/math10071014Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image SegmentationQingxin Liu0Ni Li1Heming Jia2Qi Qi3Laith Abualigah4School of Computer Science and Technology, Hainan University, Haikou 570228, ChinaSchool of Mathematics and Statistics, Hainan Normal University, Haikou 571158, ChinaSchool of Information Engineering, Sanming University, Sanming 365004, ChinaSchool of Computer Science and Technology, Hainan University, Haikou 570228, ChinaFaculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, JordanImage segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (<i>PSNR</i>), structure similarity (<i>SSIM</i>), and feature similarity (<i>FSIM</i>). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.https://www.mdpi.com/2227-7390/10/7/1014remora optimization algorithmmulti-level thresholding image segmentationcross-entropymeta-heuristicoptimization |
| spellingShingle | Qingxin Liu Ni Li Heming Jia Qi Qi Laith Abualigah Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation Mathematics remora optimization algorithm multi-level thresholding image segmentation cross-entropy meta-heuristic optimization |
| title | Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation |
| title_full | Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation |
| title_fullStr | Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation |
| title_full_unstemmed | Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation |
| title_short | Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation |
| title_sort | modified remora optimization algorithm for global optimization and multilevel thresholding image segmentation |
| topic | remora optimization algorithm multi-level thresholding image segmentation cross-entropy meta-heuristic optimization |
| url | https://www.mdpi.com/2227-7390/10/7/1014 |
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