Normalized Cuts and Image Segmentation

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grouping
image segmentation
graph partitioning
computer vision
eigenvalues and eigenfunctions
graph theory
image sequences
dissimilarity
eigenvalues
normalized cut
perceptual grouping
similarity
Electrical and Computer Engineering
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Malik, Jitendra
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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.

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2000-08-01
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Copyright 2000 IEEE. Reprinted from IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22, Issue 8, August 2000, pages 888-905. Publisher URL: http://dx.doi.org/10.1109/34.868688 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Jianbo Shi was affiliated with Carnegie Mellon University. Currently (March 2005), he is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania.
Copyright 2000 IEEE. Reprinted from IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22, Issue 8, August 2000, pages 888-905. Publisher URL: http://dx.doi.org/10.1109/34.868688 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Jianbo Shi was affiliated with Carnegie Mellon University. Currently (March 2005), he is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania.
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