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

Highlights • A generalized 2D multi-level thresholding criterion function is proved rigorously by mathematical induction method. • A multi-level thresholding scheme for a RGB color image is proposed. • PSO algorithm is applied to seek to optimal threshold values in a very reasonable computational time. • The segmented image is compared with the human segmentation from BSDS300 to evaluate the experiment results quantitatively and objectively. Abstract In this paper, we propose a multi-level thresholding model based on gray-level & local-average histogram (GLLA) and Tsallis–Havrda–Charvát 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. [ABSTRACT FROM AUTHOR]