Particle Swarm Optimization Based Spatial Credibilistic Clustering Algorithm Applied in High Noise Image Segmentation

In practice, noise images even high noise images are very common. It’s very essential and critical to deal with such kind of 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 noise images are studied and the research shows that in most case, CCA performs better than FCM in high noise image segmentation. Based on that, a new kind of fuzzy clustering method is presented. It combines spatial credibilistic clustering algorithm (SCCA) with particle swarm optimization (PSO) and takes full advantages of them. The advantage of CCA in noise image segmentation is also suitable for SCCA, and the imposition of spatial information enlarges the advantage. The combination of PSO helps to improve global search performance, thereby, the novel methods overcome the drawback of single clustering method local optimal solution. Computational experiments show that the proposed methods give the best segmentation results when comparing with fuzzy c-means algorithm, credibilistic clustering algorithm, spatial credibilistic clustering algorithm and the PSO incorporated versions of FCM and CCA.