Weighted Bayesian Network for visual tracking

Yue Zhou, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bayesian Network has been shown to be very successful for many computer vision applications, most of which are solved using the generative approaches. We propose a novel Weighted Bayesian Network which relaxes the conditional independent assumption in traditional Bayesian Network by assigning weights to the estimations of conditional probabilities. In the Weighted Bayesian Network, the hidden variables are estimated generatively as in the traditional graphical models, and the weights of conditional probabilities are adjusted discriminatively from the training samples. The combined generative/ discriminative approach in a loop preserves the advantage of generative model toperform unsupervised learning and handle missing data while improve the model flexibility and performance by the discriminative learning of probability estimation weights. Our experiments show a number of real-time examples in visual tracking where the performances are significantly improved with the Weighted Bayesian Networks.

Original languageEnglish (US)
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages523-526
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Zhou, Y., & Huang, T. S. (2006). Weighted Bayesian Network for visual tracking. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 523-526). [1698946] (Proceedings - International Conference on Pattern Recognition; Vol. 1). https://doi.org/10.1109/ICPR.2006.1188