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Fast Planar Correlation Clustering for Image Segmentation

  • Julian Yarkony
  • Alexander Ihler
  • Charless C. Fowlkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

We describe a new optimization scheme for finding high-quality clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our algorithm on the problem of image segmentation where this approach outperforms existing global optimization techniques in minimizing the objective and is competitive with the state of the art in producing high-quality segmentations.

Keywords

Image Segmentation Planar Graph Integer Linear Programming Markov Random Field Linear Programming Relaxation 
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 2012

Authors and Affiliations

  • Julian Yarkony
    • 1
  • Alexander Ihler
    • 1
  • Charless C. Fowlkes
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaIrvineUSA

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