Open AccessA Multilevel Thresholding Approach Based on Improved Particle Swarm Optimization for Color Image Segmentation
Author(s) -
Larissa Britto,
Luciano D. S. Pacífico,
Teresa B. Ludermir
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12138
Subject(s) - particle swarm optimization , thresholding , image segmentation , artificial intelligence , robustness (evolution) , computer science , benchmark (surveying) , population , otsu's method , image (mathematics) , pattern recognition (psychology) , mathematical optimization , mathematics , algorithm , geography , biochemistry , chemistry , demography , geodesy , sociology , gene
In this paper, a hybrid Otsu and improved Particle Swarm Optimization (PSO) algorithm is presented to deal with multilevel color image thresholding problem, named APSOW. In APSOW, the historical information represented by the local best solutions found so far by PSO population are permuted among the current population, using a randomized greedy process. APSOW also implements a weedout operator to prune the worst individuals from the population. The proposed APSOW is compared to other hybrid EAs and Otsu approaches from literature (include standard PSO model) through twelve benchmark color image problems, showing its potential and robustness.