Interpreting Graph Cuts as a Max-Product Algorithm

Details Interpreting Graph Cuts as a Max-Product Algorithm Interpreting Graph Cuts as a Max-Product Algorithm

2011-05-05

arxiv.org/abs/1105.1178v1

Computer Science

Learning

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Interpreting Graph Cuts as a Max-Product Algorithm does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Interpreting Graph Cuts as a Max-Product Algorithm, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Interpreting Graph Cuts as a Max-Product Algorithm will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-575306