Image segmentation via multilevel thresholding using hybrid optimization algorithms

We introduce an alternative hybrid swarm algorithm for image segmentation that employs multilevel thresholding techniques. For the hybridization, we have combined the whale optimization algorithm (WOA) and the particle swarm optimization (PSO). The proposed method is called WOAPSO, and it operates in a cooperative environment, where the initial population is divided into two subpopulations (the first subpopulation is assigned for WOA and the other is assigned for PSO). Then, the WOA and the PSO operate in parallel during the iterative process to update the solutions and the best solution is selected from the union of the updated subpopulations according to the objective function. Here, two objective functions are used, the Otsu's method and the fuzzy entropy method. These functions evaluate the quality of the thresholds generated by the WOAPSO considering the variance and the entropy of the classes where the pixels are cataloged. The experimental results and comparisons provide evidence of the ability of the proposed WOAPSO algorithm to reduce the time complexity without affecting the accuracy of the solutions. (C) 2018 SPIE and IS&T