Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images

Authors

Erik Cuevas^*, , Angel Trujillo, Mario A. Navarro, Primitivo Diaz

Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, C.P 44430, Guadalajara, Jal, Mexico

^*Corresponding author. Email: erik.cuevas@cucei.udg.mx

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  -