PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING
International Journal of Pattern Recognition and Artificial IntelligencePublished 1 May 2005
Mahamed G. H. Omran, Andries P. Engelbrecht, Ayed Salman
Citations287
SJR quartileQ3
SJR score0.27
SNIP0.40
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TL;DR
An image clustering method that is based on the particle swarm optimizer (PSO) that is applied to synthetic, MRI and satellite images and shows that the PSO image classifier performs better than state-of-the-art image classifiers in all measured criteria.
Abstract
An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of different values of PSO control parameters on performance is also illustrated.
Keywords
Computer ScienceEngineering
Particle swarm optimization
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