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Bayesian Networks and the Imprecise Dirichlet Model Applied to Recognition Problems

  • Cassio P. de Campos
  • Qiang Ji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6717)

Abstract

This paper describes an Imprecise Dirichlet Model and the maximum entropy criterion to learn Bayesian network parameters under insufficient and incomplete data. The method is applied to two distinct recognition problems, namely, a facial action unit recognition and an activity recognition in video surveillance sequences. The model treats a wide range of constraints that can be specified by experts, and deals with incomplete data using an ad-hoc expectation-maximization procedure. It is also described how the same idea can be used to learn dynamic Bayesian networks. With synthetic data, we show that our proposal and widely used methods, such as the Bayesian maximum a posteriori, achieve similar accuracy. However, when real data come in place, our method performs better than the others, because it does not rely on a single prior distribution, which might be far from the best one.

Keywords

Bayesian Network Maximum Entropy Activity Recognition Facial Expression Recognition Dynamic Bayesian Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cassio P. de Campos
    • 1
  • Qiang Ji
    • 2
  1. 1.Dalle Molle Institute for Artificial IntelligenceManno-LuganoSwitzerland
  2. 2.Rensselaer Polytechnic InstituteTroyUSA

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