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

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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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Acknowledgements

This work was partially funded by OSEO, French state agency for innovation, in the framework of the Quaero project.

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Authors and Affiliations

  1. CNRS – IRISA, Campus de Beaulieu, 35042, Rennes Cedex, France

    Guillaume Gravier

  2. Technicolor, Rennes, France

    Claire-Hélène Demarty & Siwar Baghdadi

  3. INRIA, Rennes, France

    Patrick Gros

Authors
  1. Guillaume Gravier
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  2. Claire-Hélène Demarty
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  3. Siwar Baghdadi
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  4. Patrick Gros
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Correspondence to Guillaume Gravier.

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Gravier, G., Demarty, CH., Baghdadi, S. et al. Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos. Multimed Tools Appl 70, 1421–1437 (2014). https://doi.org/10.1007/s11042-012-1169-y

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