Improved fuzzy clustering for image segmentation based on local and non-local information

Abstract

Image segmentation is the basis of image analysis, image understanding, video tracking, and etc. However, the complexity of images makes this problem difficult. In this paper, image segmentation algorithms based on fuzzy clustering are investigated and one improved schema is presented. In the proposed schema, local information and non-local information is fused into fuzzy clustering simultaneously, resulting in simple but effective segmentation algorithms. Based on non-local information, the improved algorithms can resist the effect of image artifacts, while image details can be retained with the help of neighbor information. Compared with current segmentation algorithms based on fuzzy clustering, the proposed algorithms can retrieve satisfactory results with acceptable efficiency. Experiments on different images illustrate that the proposed algorithms outperform corresponding fuzzy clustering algorithms.