Globally Optimal Image Partitioning by Multicuts

  • Jörg Hendrik Kappes
  • Markus Speth
  • Björn Andres
  • Gerhard Reinelt
  • Christoph Schn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6819)

Abstract

We introduce an approach to both image labeling and unsupervised image partitioning as different instances of the multicut problem, together with an algorithm returning globally optimal solutions. For image labeling, the approach provides a valid alternative. For unsupervised image partitioning, the approach outperforms state-of-the-art labeling methods with respect to both optimality and runtime, and additionally returns competitive performance measures for the Berkeley Segmentation Dataset as reported in the literature.

Keywords

image segmentation partitioning labeling multicuts multiway cuts multicut polytope cutting planes integer programming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jörg Hendrik Kappes
    • 1
  • Markus Speth
    • 1
  • Björn Andres
    • 1
  • Gerhard Reinelt
    • 1
  • Christoph Schn
    • 1
  1. 1.Image & Pattern Analysis Group, Discrete and Combinatorial Optimization Group, Multidimensional Image Processing GroupUniversity of HeidelbergGermany

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