Fuzzy c-partition using particle swarm optimization algorithm

The fuzzy c-partition entropy approach for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper we applied fuzzy entropy in image segmentation, used it to select the fuzzy region of membership function automatically so that an image can be transformed into fuzz domain with maximum fuzzy entropy, and implemented particle swarm optimization algorithm to find the optimal combination of fuzzy parameters. The proposed fast approach has been tested on many images for example the processing time of tri level thresholding of each image is reduced from more than 22 min to less than 0.5 s. The Fuzzy c-partition entropy using PSO algorithm perform equally well in terms of the quality of image segmentation and leads to a good visual result.