Hybrid Methods of Spatial Credibilistic Clustering and Particle Swarm Optimization in High Noise Image Segmentation

10.30000/IJFS.200809.0006

Pei-Han Wen；Jian Zhou；Li Zheng

Fuzzy clustering ； noise image segmentation ； particle swarm optimization ； spatial credibilistic clustering algorithm

International Journal of Fuzzy Systems

10卷3期（2008 / 09 / 01）

174 - 184

英文

In practice, noisy images (even high noise images) are very common. It's very essential and critical to deal with such images to process real-image segmentation and pattern recognition. In this paper, differences of credibilistic clustering algorithm (CCA) and fuzzy c-means algorithm (FCM) in dealing with noisy images are studied and the research shows that in most cases, CCA performs better than FCM in high noise image segmentation. Based on that, a new kind of fuzzy clustering methods is presented. It combines spatial credibilistic clustering algorithm (SCCA) with particle swarm optimization (PSO) and takes full advantages of them. The advantages that come from CCA in noise image segmentation also help in SCCA, and the imposition of spatial information enlarges the advantage. The addition of PSO helps to improve global search performance; thereby the novel methods overcome the drawback of single clustering methods-local optimal solutions. Computational experiments show that the proposed methods give the best segmentation results when compared with FCM, CCA, spatial fuzzy c-means algorithm (SFCM), SCCA and the PSO incorporated versions of FCM, CCA, and SFCM.