Isoperimetric Graph Partitioning for Data Clustering and Image Segmentation
| Data(s) |
14/11/2011
14/11/2011
01/07/2003
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| Resumo |
Spectral methods of graph partitioning have been shown to provide a powerful approach to the image segmentation problem. In this paper, we adopt a different approach, based on estimating the isoperimetric constant of an image graph. Our algorithm produces the high quality segmentations and data clustering of spectral methods, but with improved speed and stability. Office of Naval Research (N00014-01-1-0624) |
| Identificador | |
| Idioma(s) |
en_US |
| Publicador |
Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems |
| Relação |
BU CAS/CNS Technical Reports;CAS/CNS-TR-2003-015 |
| Direitos |
Copyright 2003 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission. Boston University Trustees |
| Tipo |
Technical Report |
