ENHANCED GRAPH BASED NORMALIZED CUT METHODS FOR IMAGE SEGMENTATION

Image segmentation is one of the important steps in digital image processing. Several algorithms are available for segmenting the images, posing many challenges such as precise criteria and efficient computations. Most of the graph based methods used for segmentation depend on local properties of graphs without considering global impressions of image, which ultimately limits segmentation quality. In this paper, we propose an enhanced graph based normalized cut method for extracting global impression and consistencies in the image. We propose a technique to add flexibility to original recursive normalized two way cut method which was further extended to other graph based methods. The results show that the proposed technique improves segmentation quality as well as requires lesser computational time than the regular normalized cut method.