Multilevel Thresholding Image Segmentation Based on Multi-swarm Particle Swarm optimization with a Dynamic Learning Strategy and Kapur’s entropy
In this paper, the multilevel thresholding method based on Kapur’s entropy and the recently proposed multi-swarm particle swarm optimization with a dynamic learning strategy is considered. The multilevel thresholding method is extensively evaluated on ten benchmark images. The experimental results, which include the mean and standard deviation of Kapur’s entropy, obtained from forty independent executions of the thresholding method for each test image and the considered total number of thresholds, demonstrate that multi-swarm particle swarm optimization with dynamic learning strategy can be successfully applied to solve the multilevel thresholding problem.