Journal of Theoretical and Applied Information Technology © 2005 - 2010JATIT. All rights reserved. www.jatit.org 1 NOVEL GRAPH BASED METHOD FOR IMAGE SEGMENTATION 1 Dr. S. V.KASMIR RAJA, 2 A. SHAIK ABDUL KHADIR, 3 Dr. S. S. RIAZ AHAMED 1 Dean (Research), SRM University, Chennai, TamilNadu, India 2 Lecturer (SG), Dept of Computer Science, Khadir Mohideen College, Adirampattinam-614701, TamilNadu, India-614701 3 Principal, Sathak Institute of Technology, Ramanathapuram,TamilNadu, India-623501. Email: ssriaz@ieee.org , ssriaz@yahoo.com ABSTRACT 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(N 3 ). 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. Keywords : Grouping, image segmentation, graph partitioning., unsupervised clustering 1. INTRODUCTION Nearly 75 years ago, Wertheimer [1] pointed out the importance of perceptual grouping and organization in vision and listed several key factors, such as similarity, proximity, and good continuation, which lead to visual grouping. However, even to this day, many of the computational issues of perceptual grouping have remained unresolved. In this paper, we present a general framework for this problem, focusing specifically on the case of image segmentation. Prior literature on the related problems of clustering, grouping and image segmentation is huge. The clustering community [3] has offered us agglomerative and divisive algorithms; in image segmentation, we have region-based merge and split algorithms. The hierarchical divisive approach that we advocate produces a tree, the dendrogram. While most of these ideas go back to the 1970s (and earlier), the 1980s brought in the use of Markov Random Fields [2] and variational formulations [6], [4], [5]. Data Clustering and graph segmentation are important operations for machine learning and computer vision. It includes both unsupervised and semi-supervised clustering. For unsupervised clustering, spectral method[8][9][10][7] has been the focus of considerable research. However, all these methods suffer from the slow computation of large matrix. Solving systems of linear equations Ax = b and computing eigenvalues and eigenvectors of large matrices Ax=λx are two fundamental computation for clustering problem. They also have great practical uses in other machine learning problems. For example, for semi- supervised clustering, to label unlabeled points can be transformed into a problem of solving linear systems. And for the spectral clustering problem, it turns out to be a problem of computing the largest k eigenvectors of the weighted matrix W. Hence how to solve these two problems correctly and fast becomes very important.