Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation

Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. In this work, a new image segmentation method has been introduced based on the constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image histogram act as searcher agents of the CPSOGSA. Besides, the optimal number of thresholds is determined using Kapur’s entropy method. The effectiveness and applicability of CPSOGSA have been accomplished by applying it to four standard images from the USC-SIPI image database including airplane, cameraman, clock, and truck. Various performance metrics have been employed to investigate the simulation outcomes including optimal thresholds, standard deviation, mean, run-time analysis, PSNR (peak signal-to-noise ratio), best fitness value calculation, convergence maps, and box plot analysis. In addition, the experimental results of CPSOGSA are compared with standard PSO and GSA. The simulation results clearly indicate that hybrid CPSOGSA takes less computational time in finding the best threshold values of the benchmark images.