Multi-Objective Evolutionary Algorithm for Image Segmentation

Abstract

Clustering is an unsupervised learning technique commonly used for image segmentation. As the outcome of most clustering algorithms is heavily dependent on the initial cluster centers, it is necessary to consider optimization during the process of segmentation. The Multi-Objective evolutionary algorithm (MOEA) was used for optimization in this study, to find optimal cluster centers. It is important to note that the effectiveness of MOEA is dependent upon the selection of objective functions. Two objectives were considered; namely, the minimization of intra-cluster compactness and the maximization of inter-cluster separation to determine the optimal initial cluster centers. XieBeni index (XBI) was used to measure the compactness and separation of cluster centers while the Average Inter-Cluster Separation (AIS) measure ensures the minimal overlapping of clusters. The MOEA will generate a set of non-dominated solutions. The Davies-Bouldin Index (DBI) is then employed to determine the most optimal solution for the cluster centers. Experimental results demonstrate that this method of image segmentation performs better than single-objective optimization (SOO)and Possibilistic Clustering Algorithm (PCA).