Color image segmentation based on multi-level Tsallis-Havrda-Charvat entropy and 2D histogram using PSO algorithms

In this paper, we propose a multi-level thresholding model based on gray-level & local-average histogram (GLLA) and Tsallis-Havrda-Charvat entropy for RGB color image. We validate the multi-level thresholding formulation by using the mathematical induction method. We apply particle swarm optimization (PSO) algorithm to obtain the optimal threshold values for each component of a RGB image. By assigning the mean values from each thresholded class, we obtain three segmented component images independently. We conduct the experiments extensively on The Berkeley Segmentation Dataset and Benchmark (BSDS300) and calculate the average four performance indices (BDE, PRI, GCE and VOI) to show the effectiveness and reasonability of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.