Convex multi-class image labeling by simplex-constrained total variation

  • Lellmann J
  • Kappes J
  • Yuan J
 et al. 
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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.

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  • Jan Lellmann

  • Jörg Kappes

  • Jing Yuan

  • Florian Becker

  • Christoph Schnörr

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