Departmental Papers (CIS)

Document Type

Journal Article

Date of this Version

8-1-2000

Comments

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

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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.

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 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.

Keywords

grouping, image segmentation, graph partitioning, computer vision, eigenvalues and eigenfunctions, graph theory, image sequences, dissimilarity, eigenvalues, normalized cut, perceptual grouping, similarity

 

Date Posted: 30 April 2005

This document has been peer reviewed.