Multi-swarm Particle Swarm Optimization with Chaotic Random Inertia Weight and Dynamic Learning Strategy for Multilevel Thresholding Image Segmentation

This paper presents a multilevel thresholding method based on the multi-swarm particle swarm optimization with dynamic learning strategy and chaotic random inertia weight. This multilevel thresholding method is implemented using Kapur’s entropy. The performance of the presented method is validated on a set of standard test images. For each image and each considered number of threshold levels, the mean and standard deviation of Kapur’s entropy values are determined based on 30 independent applications of the thresholding method. The reported experimental results show that the presented method can be successfully applied across different images.