An unsupervised multi-swarm clustering technique for image segmentation

Methods based on Particle Swarm Optimization represent efficient tools to solve a wide class of problems. In particular, they have been successfully applied to data clustering and image processing. In this paper a multi-swarm clustering technique to perform an image segmentation is proposed. The search of the gray levels segmenting the image is carried out by a two-stage procedure. The former is performed by a traditional swarm population, moving in the search space according to a minimum distance criterion. The latter exploits a structure composed by identical swarms that refine the solution of the previous step. The combination of the two swarm approaches allows to tackle the drawbacks of the classical paradigm without making use of a complex implementation. The method is unsupervised, since it identifies the actual number of gray levels to segment the image automatically. Such characteristic is fundamental in the application of image segmentation to real cases, where generally the optimal number of centers is not known a priori and the algorithms are required to face possible environment variations. The conducted experiments show that the proposed technique is able to yield adequate segmentations with a limited computational time, proving to be an interesting tool to face cases in which urgent time constraints have to be satisfied.