Image Segmentation Based on Bio-Inspired Optimization Algorithms

Abstract

This chapter addresses the issue of image segmentation by clustering in the domain of image processing. Fuzzy C-Means is a widely adopted clustering algorithm. Bio-inspired optimization algorithms are optimal methods inspired by the principles or behaviors of biology. For the purpose of reinforcing the global search capability of FCM, five Bio-Inspired Optimization Algorithms (BIOA) including Biogeography-Based Optimization (BBO), Artificial Fish School Algorithm (AFSA), Artificial Bees Colony (ABC), Particle Swarm Optimization (PSO), and Bacterial Foraging Algorithm (BFA) are used to optimize the objective criterion function, which is interrelated to centroids in FCM. The optimized FCMs by the five algorithms are used for image segmentation, respectively. They have different effects on the results.