An Efficient Dynamic Image Segmentation Algorithm Using a Hybrid Technique Based on Particle Swarm Optimization and Genetic Algorithm

This paper describe a new approach to automatic unsupervised efficient image segmentation algorithm using hybrid technique based on Particle Swarm Optimization and Genetic Algorithm. This technique uses the PSO based dynamic clustering approach to predict the optimal number clusters which is required to partition the data set. This prediction is then used by the GA based module to improve the final result (global best particle) of the PSO based method. The best number of clusters is obtained by using cluster validity criterion with the help of Gaussian distribution. The proposed algorithm is evaluated on well known natural images and its performance is compared to that of DCPSO, snob and SOM based clustering techniques. Experimental results demonstrate the performance of the proposed algorithm producing comparable segmentation results.