A New Framework for Approximate Labeling via Graph Cuts

Authors:
Nikos Komodakis;Georgios Tziritas
Affiliations:
University of Crete;University of Crete
Venue:
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Year:
2005

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Abstract

A new framework is presented that uses tools from duality theory of linear programming to derive graph-cut based combinatorial algorithms for approximating NP-hard classification problems. The derived algorithms include 驴-expansion graph cut techniques merely as a special case, have guaranteed optimality properties even in cases where á-expansion techniques fail to do so and can provide very tight per-instance suboptimality bounds in all occasions.