A Survey Paper on Learning Pullback HMM Distance for Recognition of Action
Journal: International Journal of Science and Research (IJSR) (Vol.3, No. 11)Publication Date: 2014-11-05
Authors : Vanita Babane; Poonam Sangar;
Page : 2225-2226
Keywords : Distance learning; pullback metrics; hidden Markov models HMM; action recognition;
Abstract
Recent work in action recognition has exposed the limitations of features extracted from spatiotemporal video volumes. Whereas, encoding the actions dynamics using generative dynamical models has a number of attractive features, in this respect Hidden Markov models (HMMs) is a popular choice. A general framework based on pullback metrics for learning distance functions of a given training set of labeled videos has been generated, The optimal distance function is selected among a family of pullback ones, which is generated by a parameterized automorphism of the space models. An experimental result shows that how pullback learning greatly improves action recognition performances with respect to base distances.
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