Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos |
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Authors: | Guillaume Gravier Claire-Hélène Demarty Siwar Baghdadi Patrick Gros |
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Affiliation: | 1. CNRS – IRISA, Campus de Beaulieu, 35042, Rennes Cedex, France 2. Technicolor, Rennes, France 3. INRIA, Rennes, France
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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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