ABSTRACT
This chapter presents an overview of various ways to carry out image segmentation using swarm intelligence (SI) approaches with their performance evaluations. In this chapter, two predominantly used techniques are considered for segmentation of regions of interest in images. They are the ant colony optimization and particle swarm optimization techniques. These techniques are combined with the Fuzzy C-Mean (FCM) clustering algorithm to enhance the performance of the proposed approach. In 1992, the ant colony optimization (ACO) was proposed by Marco Dorigo et al. It is a population-based metaheuristic approach used to find the appropriate solution for complex optimization problems. The particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique for the solution of continuous optimization problems. The chapter also describes the ant colony optimization with the fuzzy morphological-based fusion (FMF) algorithm and its performance. Due to these clustering algorithms, each initialized cluster center will be diminished over a total number of iterations.
