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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Fast Memory-Efficient Generalized Belief Propagation
verfasst von : M. Pawan Kumar, P. H. S. Torr
Erschienen in: Computer Vision – ECCV 2006
Verlag: Springer Berlin Heidelberg
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Generalized Belief Propagation (
gbp
) has proven to be a promising technique for performing inference on Markov random fields (
mrf
s). 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
mrf
s 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.