A Survey Paper on Learning Pullback HMM Distance for Recognition of Action

Vanita Babane, 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

How to Cite?: Vanita Babane, Poonam Sangar, "A Survey Paper on Learning Pullback HMM Distance for Recognition of Action", Volume 3 Issue 11, November 2014, International Journal of Science and Research (IJSR), Pages: 2225-2226, https://www.ijsr.net/getabstract.php?paperid=OCT141446, DOI: https://dx.doi.org/10.21275/OCT141446