Uncertainty in Artificial Intelligence
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MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
M. Kumar, Daphne Koller
We consider the task of obtaining the maximum a posteriori estimate of discrete pairwise random fields with arbitrary unary potentials and semimetric pairwise potentials. For this problem, we propose an accurate hierarchical move making strategy where each move is computed efficiently by solving an st-MINCUT problem. Unlike previous move making approaches, e.g. the widely used a-expansion algorithm, our method obtains the guarantees of the standard linear programming (LP) relaxation for the important special case of metric labeling. Unlike the existing LP relaxation solvers, e.g. interior-point algorithms or tree-reweighted message passing, our method is significantly faster as it uses only the efficient st-MINCUT algorithm in its design. Using both synthetic and real data experiments, we show that our technique outperforms several commonly used algorithms.
Keywords: null
Pages: 313-320
PS Link:
PDF Link: /papers/09/p313-kumar.pdf
AUTHOR = "M. Kumar and Daphne Koller",
TITLE = "MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "313--320"

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