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German Conference on Pattern Recognition

GCPR 2018: Pattern Recognition pp 698–713Cite as

Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

Abstract

Manifold models of image features abound in computer vision. We present a novel approach that combines unsupervised computation of representative manifold-valued features, called labels, and the spatially regularized assignment of these labels to given manifold-valued data. Both processes evolve dynamically through two Riemannian gradient flows that are coupled. The representation of labels and assignment variables are kept separate, to enable the flexible application to various manifold data models. As a case study, we apply our approach to the unsupervised learning of covariance descriptors on the positive definite matrix manifold, through spatially regularized geometric assignment.

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Acknowledgements

This work was supported by the German Research Foundation (DFG), grant GRK 1653.

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Authors and Affiliations

  1. Image and Pattern Analysis Group, Heidelberg University, Heidelberg, Germany

    Artjom Zern, Matthias Zisler, Freddie Åström & Christoph Schnörr

  2. Mathematical Imaging Group, Heidelberg University, Heidelberg, Germany

    Stefania Petra

Authors
  1. Artjom Zern
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  2. Matthias Zisler
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  3. Freddie Åström
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  4. Stefania Petra
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  5. Christoph Schnörr
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Corresponding author

Correspondence to Artjom Zern .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Zern, A., Zisler, M., Åström, F., Petra, S., Schnörr, C. (2019). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_48

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12938-5

  • Online ISBN: 978-3-030-12939-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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