Automatic image clustering using a swarm intelligence approach

In order to implement clustering under the condition that the number of clusters is not known a priori, we propose in this paper ACPSO a novel automatic image clustering algorithm based on particle swarm optimization algorithm. ACPSO can partition image into compact and well separated clusters without any knowledge on the real number of clusters. ACPSO used a novel representation scheme for the search variables in order to determine the optimal number of clusters. The partition of each particle of the swarm evolves using evolving operators which aim to reduce dynamically the number of clusters centers. Experimental results on real images demonstrate the effectiveness of the proposed approach.