Abstract: In order to improve the segmentation performance when the foreground and the background are similar, an interactive image segmentation method based on fuzzy C-means and graph cuts algorithm is proposed. Firstly, the image is pre-segmented into a large number of small partitions using watershed algorithm which will be used instead of image pixels. Then, cluster centers of the foreground and background are calculated by the fuzzy C-means method. Finally, the likelihood energy is determined by the minimum distances from the unlabelled node to the foreground and background clusters, and the prior energy is determined by the relationship between the unlabelled node and its neighbor areas. The maximum-flow/minimum-cut algorithm is used to solve the global optimization problem. Compared with the results of other method, the experimental results show that our method can obtain a better segmentation performance with easy user interaction even though the foreground and background are similar.

Key words: computer application, image segmentation, fuzzy C-means, interactive image segmentation