Paper Title
Feature Model Based Image Segmentation Using Normalized Graph Cut

This work deals with the graph based segmentation of image using intensity, color and texture feature model. Graph cut is one of the most efficient segmentation techniques which consider spatial feature models based on intensity, color and texture to get Eigen vector decomposition for image segmentation. This paper focuses on the use of a weighted Euclidean distance based on each feature model to calculate the edge weight matrix for graph cut image segmentation, which gives prominent results for color feature model.