Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images
Shape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature. Classical image processing techniques have been normally used to solve this problem. Alternatively, shape recognition has also been conducted...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-08-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125942973/view |
| _version_ | 1818228790935945216 |
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| author | Erik Cuevas Angel Trujillo Mario A. Navarro Primitivo Diaz |
| author_facet | Erik Cuevas Angel Trujillo Mario A. Navarro Primitivo Diaz |
| author_sort | Erik Cuevas |
| collection | DOAJ |
| description | Shape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature. Classical image processing techniques have been normally used to solve this problem. Alternatively, shape recognition has also been conducted through metaheuristic algorithms. They have demonstrated to have a competitive performance in terms of robustness and accuracy. However, all of these schemes use old metaheuristic algorithms as the basis to identify geometrical structures in images. Original metaheuristic approaches experiment several limitations such as premature convergence and low diversity. Through the introduction of new models and evolutionary operators, recent metaheuristic methods have addressed these difficulties providing in general better results. This paper presents a comparative analysis on the application of five recent metaheuristic schemes to the shape recognition problem such as the Grey Wolf Optimizer (GWO), Whale Optimizer Algorithm (WOA), Crow Search Algorithm (CSA), Gravitational Search Algorithm (GSA) and Cuckoo Search (CS). Since such approaches have been successful in several new applications, the objective is to determine their efficiency when they face a complex problem such as shape detection. Numerical simulations, performed on a set of experiments composed of images with different difficulty levels, demonstrates the capacities of each approach. |
| first_indexed | 2024-12-12T10:08:19Z |
| format | Article |
| id | doaj.art-3f97aaa1298b418185c002f3e179210c |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-12T10:08:19Z |
| publishDate | 2020-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-3f97aaa1298b418185c002f3e179210c2022-12-22T00:27:52ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-08-0113110.2991/ijcis.d.200729.001Comparison of Recent Metaheuristic Algorithms for Shape Detection in ImagesErik CuevasAngel TrujilloMario A. NavarroPrimitivo DiazShape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature. Classical image processing techniques have been normally used to solve this problem. Alternatively, shape recognition has also been conducted through metaheuristic algorithms. They have demonstrated to have a competitive performance in terms of robustness and accuracy. However, all of these schemes use old metaheuristic algorithms as the basis to identify geometrical structures in images. Original metaheuristic approaches experiment several limitations such as premature convergence and low diversity. Through the introduction of new models and evolutionary operators, recent metaheuristic methods have addressed these difficulties providing in general better results. This paper presents a comparative analysis on the application of five recent metaheuristic schemes to the shape recognition problem such as the Grey Wolf Optimizer (GWO), Whale Optimizer Algorithm (WOA), Crow Search Algorithm (CSA), Gravitational Search Algorithm (GSA) and Cuckoo Search (CS). Since such approaches have been successful in several new applications, the objective is to determine their efficiency when they face a complex problem such as shape detection. Numerical simulations, performed on a set of experiments composed of images with different difficulty levels, demonstrates the capacities of each approach.https://www.atlantis-press.com/article/125942973/viewMetaheuristicsShape detectionImage processingMachine learning |
| spellingShingle | Erik Cuevas Angel Trujillo Mario A. Navarro Primitivo Diaz Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images International Journal of Computational Intelligence Systems Metaheuristics Shape detection Image processing Machine learning |
| title | Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images |
| title_full | Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images |
| title_fullStr | Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images |
| title_full_unstemmed | Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images |
| title_short | Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images |
| title_sort | comparison of recent metaheuristic algorithms for shape detection in images |
| topic | Metaheuristics Shape detection Image processing Machine learning |
| url | https://www.atlantis-press.com/article/125942973/view |
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