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Unsupervised Image Partitioning with Semidefinite Programming

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Pattern Recognition (DAGM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2449))

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Abstract

We apply a novel optimization technique, semidefinite programming, to the unsupervised partitioning of images. Representing images by graphs which encode pairwise (dis)similarities of local image features, a partition of the image into coherent groups is computed by determining optimal balanced graph cuts. Unlike recent work in the literature, we do not make any assumption concerning the objective criterion like metric pairwise interactions, for example. Moreover, no tuning parameter is necessary to compute the solution. We prove that, from the optimization point of view, our approach cannot perform worse than spectral relaxation approaches which, conversely, may completely fail for the unsupervised choice of the eigenvector threshold.

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Author information

Authors and Affiliations

  1. CVGPR-Group, Dept. Math. and Comp. Science, University of Mannheim, D-68131, Mannheim, Germany

    Jens Keuchel, Christoph Schnörr, Christian Schellewald & Daniel Cremers

Authors
  1. Jens Keuchel
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  2. Christoph Schnörr
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  3. Christian Schellewald
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  4. Daniel Cremers
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Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, ETH Zürich, Gloriastrasse 35, 8092, Zürich, Switzerland

    Luc Van Gool

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© 2002 Springer-Verlag Berlin Heidelberg

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Keuchel, J., Schnörr, C., Schellewald, C., Cremers, D. (2002). Unsupervised Image Partitioning with Semidefinite Programming. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_18

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  • DOI: https://doi.org/10.1007/3-540-45783-6_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44209-7

  • Online ISBN: 978-3-540-45783-1

  • eBook Packages: Springer Book Archive

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