A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. The meticulous search for the best values for the optimization of fitness function using classical operations needs profuse computational time, which also results in inaccuracy and instability. In this paper, a new beta differential evolution (BDE)-based fast color image multilevel thresholding scheme using two objective functions has been presented. The optimal threshold values are determined by maximizing Kapur's and Tsallis entropy (entropy criterion) thresholding functions coupled with BDE algorithm. The efficiency of the proposed method is examined over existing multilevel thresholding methods such as artificial bee colony, particle swarm optimization, wind-driven optimization and differential evolution. These approaches are aimed to determine optimum threshold values at different levels of thresholding for color image segmentation. The proficiency of the presented methodology is demonstrated visually and computationally on five real-life true color images as well as four satellite images. Experimental outcomes are exhibited in terms of the optimal threshold value, best objective function and computational cost (in seconds) for each method at different thresholding levels. Afterward, the proposed scheme is examined intensively regarding the superiority of quality. The experimentally evaluated results show that the proposed BDE-based approach for multilevel color image segmentation can accurately and efficiently examine for multiple thresholds, which are near to optimal ones searched using an exhaustive search process.