Image segmentation using fuzzy rule based system and graph cuts

This work deals with segmentation of the gray scale, color and texture images using graph cuts. From the input image, a graph is constructed using intensity, color and texture profiles of the image simultaneously. Based on nature of the image, a fuzzy rule based system is designed to find the weight that should be given to a specific image feature during graph development. The graph obtained from fuzzy rule based weighted average of different image features is further used in normalized graph cuts framework. The graph is iteratively bi-partitioned through the normalized graph cuts to get optimum partitions resulting in segmented image. The Berkeley segmentation database is used to test our algorithm and segmentation results are evaluated through probabilistic rand index and global consistency error methods along with binary classifiers based approaches like sensitivity, positive predictive value and Dice similarity coefficient. It is shown that the presented segmentation method provides effective results for most type of the images.