Multilevel color image segmentation using modified genetic algorithm (MfGA) inspired fuzzy c-means clustering

Abstract

Convergence to local minima point is one of the major disadvantages of conventional fuzzy c-means (FCM). Due to this drawback, segmentation result may hamper for not selecting the cluster centroids properly. To overcome this, a modified genetic (MfGA) algorithm is proposed to improve the performance of FCM. The optimized class levels derived from the MfGA are employed as initial input to FCM for finding global optimal solutions in a large search space. An extensive performance comparison of the proposed MfGA inspired conventional FCM and GA based FCM on two multilevel color images establishes the superiority of the proposed approach.