@article {14,
	title = {Visual Workflow Recognition Using a Variational Bayesian Treatment of Multistream Fused Hidden Markov Models},
	journal = {Circuits and Systems for Video Technology, IEEE Transactions on},
	volume = {22},
	year = {2012},
	month = {July},
	pages = {1076 -1086},
	keywords = {active learning-based visual workflow recognition, Bayes methods, classification variance, data labeling, hidden Markov models, image sequences, learned model, learning (artificial intelligence), maximum a posteriori training, maximum information gain method, maximum likelihood training, MFHMM parameters, model variance, multistream fused hidden Markov models, overfitting issues, point estimate-based methods, posterior distribution, training methods, unlabeled data, variational Bayesian treatment, video streaming, video surveillance},
	issn = {1051-8215},
	doi = {10.1109/TCSVT.2012.2189795},
	author = {Sotirios P. Chatzis and D. Kosmopoulos}
}
