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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 eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging.
grouping, image segmentation, graph partitioning, computer vision, eigenvalues and eigenfunctions, graph theory, image sequences, dissimilarity, eigenvalues, normalized cut, perceptual grouping, similarity
Jianbo Shi and Jitendra Malik, "Normalized Cuts and Image Segmentation", . August 2000.
Date Posted: 30 April 2005
This document has been peer reviewed.