Abstract
Hidden Markov Models (HMMs) are very popular generative models for sequence data. Recent research has, however, shown that Conditional Random Fields (CRFs), a type of discriminative model, outperform HMMs in many tasks. We have previously proposed Hierarchical Hidden Conditional Random Fields (HHCRFs), a discriminative model corresponding to hierarchical HMMs (HHMMs). Given observations, HHCRFs model the conditional probability of the states at the upper levels. States at the lower levels are hidden and marginalized in the model definition. In addition, we have developed a parameter learning algorithm that requires only the states at the upper levels in the training data. Previously we applied HHCRFs to the segmentation of electroencephalographic (EEG) data for a Brain-Computer Interface, and showed that HHCRFs outperform HHMMs. In this paper, we apply HHCRFs to labeling artificial data and sports video segmentation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning, pp. 633–723.
Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden Markov model: Analysis and applications. Machine Learning 32(1), 41–62 (1998)
Murphy, K., Paskin, M.: Linear time inference in hierarchical HMMs. In: Advances in Neural Information Processing Systems, vol. 14, pp. 833–840 (2001)
Sugiura, T., Gotou, N., Hayashi, A.: A discriminative model corresponding to hierarchical HMMS. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 375–384. Springer, Heidelberg (2007)
Gotou, N., Hayashi, A., Suematsu, N.: Learning with segment boundaries for hierarchical HMMs. In: Proceedings of the 3rd International Conference on Advances in Pattern Recognition, pp. 538–543 (2005)
Sutton, C., McCallum, A., Rohanimanesh, K.: Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. The Journal of Machine Learning Research 8, 693–723 (2007)
Ghahramani, Z., Jordan, M.I.: Factorial hidden Markov models. In: Advances in Neural Information Processing Systems, vol. 8, pp. 472–478 (1995)
Liao, L., Fox, D., Kautz, H.: Hierarchical conditional random fields for GPS-based activity recognition. In: Proceedings of the 12th International Symposium on Robotics Research (2005)
Gunawardana, A., Mahajan, M., Acero, A., Platt, J.C.: Hidden conditional random fields for phone classification. In: Proceedings of the European Conference on Speech Communication and Technology, pp. 1117–1120 (2005)
Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifier: A comparison of logistic regression and naive bayes. In: Advances in Neural Information Proceeding Systems, vol. 16, pp. 841–848 (2002)
Xie, L., Chang, S.F., Divakaran, A., Sun, H.: Unsupervised discovery of multilevel statistical video structures using hierarchical hidden Markov models. In: Proceedings of the 2003 International Conference on Multimedia and Expo., vol. 3, pp. III– 29–III– 32(2003)
Wang, F., Ma, Y.F., Zhang, H.J., Li, J.T.: A generic framework for semantic sports video analysis using dynamic bayesian networks. In: Proceedings of the 11th International Multimedia Modelling Conference (MMM 2005), pp. 115–122 (2005)
Barnard, M., Odobez, J.M.: Sports event recognition using layered hmms. In: IEEE International Conference on Multimedia and Expo., pp. 1150–1153 (2005)
Kijak, E., Oisel, L., Gros, P.: Hierarchical structure analysis of sport videos using hmms. In: Proceedings of the 2003 International Conference on Image Processing, vol. 3, pp. II– 1025–II– 1028(2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tamada, H., Hayashi, A. (2009). Sports Video Segmentation Using a Hierarchical Hidden CRF. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_87
Download citation
DOI: https://doi.org/10.1007/978-3-642-02490-0_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
eBook Packages: Computer ScienceComputer Science (R0)