Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos

Guillaume Gravier 1 Claire-Hélène Demarty 2 Siwar Baghdadi 1, 2 Patrick Gros 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We investigate the use of structure learning in Bayesian networks for a complex multimodal task of action detection in soccer videos. We illustrate that classical score-oriented structure learning algorithms, such as the K2 one whose usefulness has been demonstrated on simple tasks, fail in providing a good network structure for classification tasks where many correlated observed variables are necessary to make a decision. We then compare several structure learning objective functions, which aim at finding out the structure that yields the best classification results, extending existing solutions in the literature. Experimental results on a comprehensive data set of 7 videos show that a discriminative objective function based on conditional likelihood yields the best results, while augmented approaches offer a good compromise between learning speed and classification accuracy.
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Guillaume Gravier, Claire-Hélène Demarty, Siwar Baghdadi, Patrick Gros. Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos. Multimedia Tools and Applications, Springer Verlag, 2012. ⟨hal-00712589⟩

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