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Interpreting Graph Cuts as a Max-Product Algorithm

By Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel and Brendan J. Frey


The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexample where MP converges to a suboptimal fixed point (Kulesza & Pereira, 2008). In this work, we show that under a particular scheduling and damping scheme, MP is equivalent to graph cuts, and thus optimal. We explain the apparent contradiction by showing that with proper scheduling and damping, MP always converges to an optimal fixed point. Thus, the canonical counterexample only shows the suboptimality of MP with a particular suboptimal choice of schedule and damping. With proper choices, MP is optimal

Topics: Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Statistics - Machine Learning
Year: 2011
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