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Learning Pullback HMM Distances
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ISSN: 0162-8828
Fabio Cuzzolin, Oxford Brookes University, Oxford
Michael Sapienza, Oxford Brookes University, Oxford
Recent work in action recognition has exposed the limitations of methods which directly classify local features extracted from spatio-temporal video volumes. In opposition, encoding the actions' dynamics via generative dynamical models has a number of attractive features: however, using all-purpose distances for their classification does not necessarily deliver good results. We propose a general framework for learning distance functions for generative dynamical models, given a training set of labelled videos. The optimal distance function is selected among a family of pullback ones, induced by a parametrised automorphism of the space of models. We focus here on hidden Markov models and their model space, and design an appropriate automorphism there. Experimental results are presented which show how pullback learning greatly improves action recognition performances with respect to base distances.
Index Terms:
Hidden Markov models,Vectors,Measurement,Training,Manifolds,Covariance matrices,Feature extraction,Face and gesture recognition,Machine learning
Citation:
Fabio Cuzzolin, Michael Sapienza, "Learning Pullback HMM Distances," IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 Nov. 2013. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.181>
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