Convex Multi-class Image Labeling by Simplex-Constrained Total Variation

  • Jan Lellmann
  • Jörg Kappes
  • Jing Yuan
  • Florian Becker
  • Christoph Schnörr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5567)

Abstract

Multi-class labeling is one of the core problems in image analysis. We show how this combinatorial problem can be approximately solved using tools from convex optimization. We suggest a novel functional based on a multidimensional total variation formulation, allowing for a broad range of data terms. Optimization is carried out in the operator splitting framework using Douglas-Rachford Splitting. In this connection, we compare two methods to solve the Rudin-Osher-Fatemi type subproblems and demonstrate the performance of our approach on single- and multichannel images.

Keywords

Maximal Monotone Convex Optimization Problem Data Term Total Variation Minimization Total Variation Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jan Lellmann
    • 1
  • Jörg Kappes
    • 1
  • Jing Yuan
    • 1
  • Florian Becker
    • 1
  • Christoph Schnörr
    • 1
  1. 1.Image and Pattern Analysis Group (IPA) HCI, Dept. of Mathematics and Computer ScienceUniversity of HeidelbergGermany

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