Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system

This research work deals with the segmentation of grey scale, colour and texture images using graph-based method. A graph is constructed using intensity, colour and texture profiles of image simultaneously. Based on nature of the image, a fuzzy rule-based system is used to find the weight that should be given to a specific image feature during the graph development. The fuzzy rule-based system provides a valuable approximation to cater the fact of imprecise knowledge (in our case knowledge about the involvement of a particular image feature in image). The graph is further used in multilevel graph-partitioning algorithm based on normalised graph cuts framework where it is iteratively bi-partitioned through normalised cuts to obtain optimum partitions. Multilevel algorithm makes the process fast enough to accommodate large databases as segmentation is often used in high-level image processing-techniques (i.e. object classification and recognition). Partitioned graph then results in segmented image. Berkeley segmentation database is used to experiment on the authors algorithm. The segmentation results are evaluated through probabilistic rand index and global consistency error methods. It is shown that the presented segmentation method provides effective results for most type of images.