By Topic

Visual Workflow Recognition Using a Variational Bayesian Treatment of Multistream Fused Hidden Markov Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chatzis, S.P. ; Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK ; Kosmopoulos, Dimitrios

In this paper, we provide a variational Bayesian (VB) treatment of multistream fused hidden Markov models (MFHMMs), and apply it in the context of active learning-based visual workflow recognition (WR). Contrary to training methods yielding point estimates, such as maximum likelihood or maximum a posteriori training, the VB approach provides an estimate of the posterior distribution over the MFHMM parameters. As a result, our approach provides an elegant solution toward the amelioration of the overfitting issues of point estimate-based methods. Additionally, it provides a measure of confidence in the accuracy of the learned model, thus allowing for the easy and cost-effective utilization of active learning in the context of MFHMMs. Two alternative active learning algorithms are considered in this paper: query by committee, which selects unlabeled data that minimize the classification variance, and a maximum information gain method that aims to maximize the alteration in model variance by proper data labeling. We demonstrate the efficacy of the proposed treatment of MFHMMs by examining two challenging WR scenarios, and show that the application of active learning, which is facilitated by our VB approach, allows for a significant reduction of the MFHMM training costs.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:22 ,  Issue: 7 )