Semidefinite clustering for image segmentation with a-priori knowledge

Authors:
Matthias Heiler;Jens Keuchel;Christoph Schnörr
Affiliations:
Computer Vision, Graphics, and Pattern Recognition Group, Department of Mathematics and Computer Science, University of Mannheim, Mannheim, Germany;Institute of Computational Science, ETH Zurich, Zurich;Computer Vision, Graphics, and Pattern Recognition Group, Department of Mathematics and Computer Science, University of Mannheim, Mannheim, Germany
Venue:
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Year:
2005

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Abstract

Graph-based clustering methods are successfully applied to computer vision and machine learning problems. In this paper we demonstrate how to introduce a-priori knowledge on class membership in a systematic and principled way: starting from a convex relaxation of the graph-based clustering problem we integrate information about class membership by adding linear constraints to the resulting semidefinite program. With our method, there is no need to modify the original optimization criterion, ensuring that the algorithm will always converge to a high quality clustering or image segmentation.