Abstract: Aiming at the problem that sensitivity to noise was evitable for image segmentation algorithm based on fuzzy C-means clustering, we proposed an image segmentation algorithm based on unsupervised possibilistic clustering with automatic
 weighting. Firstly, the initialization center of the possibilistic C-means clustering algorithm was determined by means of mean shift iteration, and cluster partition was automatically determined by pattern search property of possibilistic clustering. Then, the image was weighted according to the relationship of the gray values of the pixels. By correlating the weighted coefficients with the possibility of pixel noise, the influence of noise on image segmentation was reduced. The experimental results show that compared with the image segmentation algorithm based on fuzzy C-means clustering, the proposed algorithm not only achieves better segmentation result, but also has higher computational efficiency of unsupervised segmentation and more robust to noise.

Key words: image segmentation, possibilistic clustering, mean shift, robustness