A novel fuzzy clustering method based on chaos small-world algorithm for image edge detection

To solve the fuzzy edge detection problems in image processing, a novel fuzzy clustering method based on chaos small-world algorithm (CSWFCM) is presented. The traditional fuzzy clustering method (FCM) is good at local searching capability, but it is sensitive to the initial value and easy to trap into local minimum value. The small-world algorithm (SWA), inspired by the mechanism of small-world phenomenon, is a novel global searching algorithm, which enables to enhance the diversity of the population and avoid trapping into local minimum value. However, the further capability of solving complicated problems is limited for its low efficiency of local short-range searching operator. In this paper, the chaos disturbance is utilized to improve the searching efficiency of SWA after local short-range search, and the chaos small-world algorithm (CSWA) is used to optimize the FCM in image edge detection. The simulation results show that the proposed algorithm can correctly detect the fuzzy and exiguous edges with higher convergence speed.