Abstract
Conditional Random Fields and Hidden Conditional Random Fields are a staple of many sequence tagging and classification frameworks. An underlying assumption in those models is that the state sequences (tags), observed or latent, take their values from a set of nominal categories. These nominal categories typically indicate tag classes (e.g., part-of-speech tags) or clusters of similar measurements. However, in some sequence modeling settings it is more reasonable to assume that the tags indicate ordinal categories or ranks. Dynamic envelopes of sequences such as emotions or movements often exhibit intensities growing from neutral, through raising, to peak values. In this work we propose a new model family, Hidden Conditional Ordinal Random Fields (H-CORFs), that explicitly models sequence dynamics as the dynamics of ordinal categories. We formulate those models as generalizations of ordinal regressions to structured (here sequence) settings. We show how classification of entire sequences can be formulated as an instance of learning and inference in H-CORFs. In modeling the ordinal-scale latent variables, we incorporate recent binning-based strategy used for static ranking approaches, which leads to a log-nonlinear model that can be optimized by efficient quasi-Newton or stochastic gradient type searches. We demonstrate improved prediction performance achieved by the proposed models in real video classification problems.
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Chu, W., Ghahramani, Z.: Gaussian processes for ordinal regression. Journal of Machine Learning Research 6, 1019–1041 (2005)
Chu, W., Keerthi, S.S.: New approaches to support vector ordinal regression. In: International Conference on Machine Learning (2005)
Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–292 (2001)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (2005)
Gunawardana, A., Mahajan, M., Acero, A., Platt, J.C.: Hidden conditional random fields for phone classification. In: International Conference on Speech Communication and Technology (2005)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Advances in Large Margin Classifiers. MIT Press, Cambridge (2000)
Hu, Y., Li, M., Yu, N.: Multiple-instance ranking: Learning to rank images for image retrieval. In: Computer Vision and Pattern Recognition (2008)
Jing, Y., Baluja, S.: Pagerank for product image search. In: Proceeding of the 17th international conference on World Wide Web (2008)
Kumar, S., Hebert, M.: Discriminative random fields. International Journal of Computer Vision 68, 179–201 (2006)
Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning (2001)
Lien, J., Kanade, T., Cohn, J., Li, C.: Detection, tracking, and classification of action units in facial expression. Journal of Robotics and Autonomous Systems (1999)
Quattoni, A., Collins, M., Darrell, T.: Conditional random fields for object recognition. In: Neural Information Processing Systems (2004)
Shan, C., Gong, S., McOwan, P.W.: Conditional mutual information based boosting for facial expression recognition. In: British Machine Vision Conference (2005)
Shashua, A., Levin, A.: Ranking with large margin principle: Two approaches. In: Neural Information Processing Systems (2003)
Tian, Y.: Evaluation of face resolution for expression analysis. In: Computer Vision and Pattern Recognition Workshop on Face Processing in Video (2004)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision 57(2), 137–154 (2001)
Vishwanathan, S., Schraudolph, N., Schmidt, M., Murphy, K.: Accelerated training of conditional random fields with stochastic meta-descent. In: International Conference on Machine Learning (2006)
Wang, S., Quattoni, A., Morency, L.P., Demirdjian, D., Darrell, T.: Hidden conditional random fields for gesture recognition. In: Computer Vision and Pattern Recognition (2006)
Willems, G., Tuytelaars, T., Gool, L.: An efficient dense and scale-invariant spatio-temporal interest point detector. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 650–663. Springer, Heidelberg (2008)
Yang, P., Liu, Q., Metaxas, D.N.: Rankboost with l1 regularization for facial expression recognition and intensity estimation. In: International Conference on Computer Vision (2009)
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Kim, M., Pavlovic, V. (2010). Hidden Conditional Ordinal Random Fields for Sequence Classification. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15883-4_4
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DOI: https://doi.org/10.1007/978-3-642-15883-4_4
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