A Novel adaptive Discrete Cuckoo Search Algorithm for parameter optimization in computer vision

Authors

  • loubna benchikhi cadi ayad university
  • Mohamed Sadgal
  • Aziz Elfazziki
  • Fatimaezzahra Mansouri

DOI:

https://doi.org/10.4114/intartif.vol20iss60pp51-71

Keywords:

Computer vision; image processing; parameter optimization; metaheuristic; ADCS; quality control.

Abstract

Computer vision applications require choosing operators and their parameters, in order to provide the best outcomes. Often, the users quarry on expert knowledge and must experiment many combinations to find manually the best one. As performance, time and accuracy are important, it is necessary to automate parameter optimization at least for crucial operators. In this paper, a novel approach based on an adaptive discrete cuckoo search algorithm (ADCS) is proposed. It automates the process of algorithms’ setting and provides optimal parameters for vision applications. This work reconsiders a discretization problem to adapt the cuckoo search algorithm and presents the procedure of parameter optimization. Some experiments on real examples and comparisons to other metaheuristic-based approaches: particle swarm optimization (PSO), reinforcement learning (RL) and ant colony optimization (ACO) show the efficiency of this novel method.

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Author Biography

loubna benchikhi, cadi ayad university

Phd student

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Published

2017-10-17

How to Cite

benchikhi, loubna, Sadgal, M., Elfazziki, A., & Mansouri, F. (2017). A Novel adaptive Discrete Cuckoo Search Algorithm for parameter optimization in computer vision. Inteligencia Artificial, 20(60), 51–71. https://doi.org/10.4114/intartif.vol20iss60pp51-71