Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images
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- DOI
- 10.2991/ijcis.d.200729.001How to use a DOI?
- Keywords
- Metaheuristics; Shape detection; Image processing; Machine learning
- Abstract
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.
- Copyright
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Erik Cuevas AU - Angel Trujillo AU - Mario A. Navarro AU - Primitivo Diaz PY - 2020 DA - 2020/08/05 TI - Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images JO - International Journal of Computational Intelligence Systems SP - 1059 EP - 1071 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200729.001 DO - 10.2991/ijcis.d.200729.001 ID - Cuevas2020 ER -