PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast memory-efficient generalized belief propagation
Mudigonda Pawan Kumar and Philip Torr
In: ECCV 2006, 7-13 May 2006, Graz, Austria.


Generalized Belief Propagation (GBP) has proven to be a promising technique for performing inference on Markov random fields (MRFs). However, its heavy computational cost and large memory requirements have restricted its application to problems with small state spaces. We present methods for reducing both run time and storage needed by GBP for a large class of pairwise potentials of the MRF. Further, we show how the problem of subgraph matching can be formulated using this class of MRFs and thus, solved efficiently using our approach. Our results significantly outperform the state-of-the-art method. We also obtain excellent results for the related problem of matching pictorial structures for object recognition.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2100
Deposited By:Mudigonda Pawan Kumar
Deposited On:01 May 2006