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Survey Paper | Computer Science & Engineering | India | Volume 3 Issue 11, November 2014
A Survey Paper on Learning Pullback HMM Distance for Recognition of Action
Vanita Babane [3] | Poonam Sangar
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.
Keywords: Distance learning, pullback metrics, hidden Markov models HMM, action recognition
Edition: Volume 3 Issue 11, November 2014,
Pages: 2225 - 2226