Globally optimal image partitioning by multicuts

Authors:
Jörg Hendrik Kappes;Markus Speth;Björn Andres;Gerhard Reinelt;Christoph Schnörr
Affiliations:
University of Heidelberg, Germany;University of Heidelberg, Germany;University of Heidelberg, Germany;University of Heidelberg, Germany;University of Heidelberg, Germany
Venue:
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Year:
2011

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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.