Bayesian Networks and the Imprecise Dirichlet Model Applied to Recognition Problems
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.
KeywordsBayesian Network Maximum Entropy Activity Recognition Facial Expression Recognition Dynamic Bayesian Network
Unable to display preview. Download preview PDF.
- 4.de Campos, C.P., Cozman, F.G.: Belief updating and learning in semi-qualitative probabilistic networks. In: Conf. on Uncertainty in Artificial Intelligence, pp. 153–160 (2005)Google Scholar
- 11.Graca, J., Ganchev, K., Taskar, B.: Expectation maximization and posterior constraints. In: NIPS, pp. 569–576. MIT Press, Cambridge (2007)Google Scholar
- 13.Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings of the 4th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 46–53 (2000)Google Scholar
- 14.Lukasiewicz, T.: Credal networks under maximum entropy. In: Conf. on Uncertainty in Artificial Intelligence, pp. 363–370 (2000)Google Scholar
- 15.Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, Univ. of California, Berkeley (2002)Google Scholar
- 16.Niculescu, R.S., Mitchell, T.M., Rao, R.B.: A theoretical framework for learning bayesian networks with parameter inequality constraints. In: Int. Joint Conf. on Artificial Intelligence, pp. 155–160 (2007)Google Scholar
- 17.Tong, Y., Liao, W., Ji, Q.: Facial action unit recognition by exploiting their dynamic and semantic relationships. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1683–1699 (2007)Google Scholar