NOVEL GRAPH BASED METHOD FOR IMAGE SEGMENTATION

We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups.We show that an efficient computational technique based on a generalized eigen value problem can be used to optimize this criterion. At the heart of unsupervised clustering and semi-supervised clustering is the calculation of matrix Eigen values (eigenvectors) or matrix inversion. In generally, its complexity is O(N3). By using Fast Lanczos Method in Normalized cut Method, we improve the performance to O(N log N). We have applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging