Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts

  • Jörg Hendrik Kappes
  • Paul Swoboda
  • Bogdan Savchynskyy
  • Tamir Hazan
  • Christoph Schnörr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9087)

Abstract

We exploit recent progress on globally optimal MAP inference by integer programming and perturbation-based approximations of the log-partition function. This enables to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. Our approach works for any graphically represented problem instance of correlation clustering, which is demonstrated by an additional social network example.

Keywords

Correlation clustering Multicut Perturb and MAP 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jörg Hendrik Kappes
    • 1
  • Paul Swoboda
    • 2
  • Bogdan Savchynskyy
    • 1
  • Tamir Hazan
    • 3
  • Christoph Schnörr
    • 1
    • 2
  1. 1.Heidelberg Collaboratory for Image ProcessingHeidelberg UniversityHeidelbergGermany
  2. 2.Image and Pattern Analysis GroupHeidelberg UniversityHeidelbergGermany
  3. 3.Department of Computer ScienceUniversity of HaifaHaifaIsrael

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