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A Constrained Learning Method based on Ontology of Bayesian Networks for Effective Recognition of Uncertain Scenes  

Hwang, Keum-Sung (연세대학교 컴퓨터과학과)
Cho, Sung-Bae (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.6, 2007 , pp. 549-561 More about this Journal
Abstract
Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.
Keywords
Bayesian network structure; constrained learning; scene understanding; uncertain environment;
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