Nonparametric Belief Propagation (2002)
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| Venue: | IN CVPR |
| Citations: | 139 - 21 self |
BibTeX
@INPROCEEDINGS{Sudderth02nonparametricbelief,
author = {Erik B. Sudderth and Alexander T. Ihler and William T. Freeman and Alan S. Willsky},
title = {Nonparametric Belief Propagation},
booktitle = {IN CVPR},
year = {2002},
pages = {605--612},
publisher = {}
}
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Abstract
In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, non--Gaussian distributions. However, due to the limitations of existing inf#6F6F3 algorithms, it is of#]k necessary tof#3# coarse, discrete approximations to such models. In this paper, we develop a nonparametric belief propagation (NBP) algorithm, which uses stochastic methods to propagate kernel--based approximations to the true continuous messages. Each NBP message update is based on an efficient sampling procedure which can accomodate an extremely broad class of potentialf#l3]k[[z3 allowing easy adaptation to new application areas. We validate our method using comparisons to continuous BP for Gaussian networks, and an application to the stereo vision problem.
