Color Image Segmentation by Particle Swarm Optimization

Abstract

This Bachelor thesis will examine the use of the method of Particle Swarm Optimization (PSO) applied to the task of clustering the pixels of an image. In particular, we will examine if the results of the so-called MEPSO algorithm of Das et al. [7] can be replicated for color images. Furthermore, we will look at improvements in both the search space that the swarm moves through and the configuration of the interdependence over time of the particles in the swarm. Our conclusions are that the results of [7] can be replicated for color images, and that certain improvements in both the search space definition and the PSO algorithm itself can be made. Finally, we take a look at how this improved PSO algorithm compares to Soft k-means (SKM) clustering for this task.