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.
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Heiler, M., Keuchel, J., Schnörr, C. (2005). Semidefinite Clustering for Image Segmentation with A-priori Knowledge. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_39
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DOI: https://doi.org/10.1007/11550518_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
Online ISBN: 978-3-540-31942-9
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