A New Framework for Approximate Labeling via Graph Cuts

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 expansion graph cut techniques merely as a special case, have guaranteed optimality properties even in cases where -expansion techniques fail to do so and can provide very tight per-instance suboptimality bounds in all occasions.

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Nikos Komodakis, Georgios Tziritas
• Keywords: Algorithms Include Expansion, Combinatorial Algorithms, Computer Vision, Graph Cut Techniques, ICCV 2005, NP-hard Classification Problems, Optimality Properties