A hybridized clustering approach using particle swarm optimization for image segmentation

Fuzzy C-means algorithm (FCM) is the most widely used fuzzy partitioning method for data cluster. The K-means algorithm implements fast, however the result is less accurate clustering. In this paper describes a hybridized clustering approach for image segmentation using particle swarm optimization to improve the classical FCM algorithm. The experimental results show that the hybridized clustering approach can provide better effectiveness on experiments of image segmentation.