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Efficient color image segmentation using Multi-Elitist Particle Swarm Optimization Algorithm


K.M.Murugesan, S.Palaniswami


Vol. 10  No. 3  pp. 241-245


Image color classification and Image segmentation using comprehensive learning particle swarm optimization (CLPSO) technique was developed by Parag Puranik, Dr. P.R. Bajaj, Prof. P.M. Palsodkar[1],The aim was to produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. In this paper we propose Multi-Elitist Particle Swarm Optimization Algorithm (MEPSO) for Image cluster classification and segmentation. The proposed method is based on a modified version of classical Particle Swarm Optimization (PSO) algorithm, known as the Multi-Elitist PSO (MEPSO) model. It also employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original input space into homogeneous groups in a transformed high-dimensional feature space. A new particle representation scheme has been adopted for selecting the optimal number of clusters from several possible choices. The MEPSO is used to find optimal fuzzy rules and membership functions. The best fuzzy rule is selected for image segmentation.MEPSO give best rule set than standard PSO.


PSO, MEPSO, Color, Classification, Fuzzy Logic, Image Segmentation, fitness, global best, local best