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Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts
Chris Russell, L'ubor Ladicky, Pushmeet Kohli, Philip Torr
Abstract:
Markov Networks are widely used through out computer vision and machine learning. An important subclass are the Associative Markov Networks which are used in a wide variety of applications. For these networks a good approximate minimum cost solution can be found efficiently using graph cut based move making algorithms such as alpha- expansion. Recently a related model has been proposed, the associative hierarchical net- work, which provides a natural generalisation of the Associative Markov Network for higher order cliques (i.e. clique size greater than two). This method provides a good model for object class segmentation problem in com- puter vision. Within this paper we briefly describe the associative hierarchical network and provide a computationally efficient method for ap- proximate inference based on graph cuts. Our method performs well for networks con- taining hundreds of thousand of variables, and higher order potentials are defined over cliques containing tens of thousands of vari- ables. Due to the size of these problems stan- dard linear programming techniques are in- applicable. We show that our method has a bound of 4 for the solution of general as- sociative hierarchical network with arbitrary clique size noting that few results on bounds exist for the solution of labelling of Markov Networks with higher order cliques.
Keywords:
Pages: 501-508
PS Link:
PDF Link: /papers/10/p501-russell.pdf
BibTex:
@INPROCEEDINGS{Russell10,
AUTHOR = "Chris Russell
and L'ubor Ladicky and Pushmeet Kohli and Philip Torr",
TITLE = "Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "501--508"
}
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