Affiliation:
1. University Mentouri – Constantine, Algeria
2. Universite Valenciennes, France
Abstract
In order to implement clustering under the condition that the number of clusters is not known a priori, the authors propose a novel automatic clustering algorithm in this chapter, based on particle swarm optimization algorithm. ACPSO can partition images 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 naturally occurring clusters in the image as well as to refine the cluster centers. Experimental results on real images demonstrate the effectiveness of the proposed approach.
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