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