A Image Segmentation Algorithm Based on Differential Evolution Particle Swarm Optimization Fuzzy C-Means Clustering

Jiansheng Liu, Shangping Qiao

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the proposed DEPSO-FCM has strong anti-noise ability; it can improve FCM and get better image segmentation results. In particular, for the HSI color image segmentation, the DEPSO-FCM can effectively solve the instability of FCM and the error split because of the singularity of the H component.