Abstract
The vision-based scene understanding technique that infers scene-interpreting contexts from real-world vision data has to not only deal with various uncertain environments but also reflect user’s requests. Especially, learnability is a hot issue for the system. In this paper, we adopt a probabilistic approach to overcome the uncertainty, and propose an interactive learning method using combination of Bayesian network and logic network to reflect user’s requirements in real-time. The logic network works for supporting logical inference of Bayesian network. In the result of some learning experiments using interactive data, we have confirmed that the proposed interactive learning method is useful for scene context reasoning.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hwang, KS., Cho, SB. (2006). Interactive Learning of Scene Context Extractor Using Combination of Bayesian Network and Logic Network. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_104
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DOI: https://doi.org/10.1007/11864349_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
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