@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}
}
@proceedings {13,
	title = {Content-Based Time Sampling for Efficient Video Delivery over Networks of Low and Variable Bandwidth},
	journal = {Digital Telecommunications, , 2006. ICDT {\textquoteright}06. International Conference on},
	year = {2006},
	month = {Aug.},
	keywords = {bandwidth allocation, computational complexity, content management, content-based time sampling, data compression, decoding, image sampling, image sequences, linear frame skipping method, low bandwidth, MPEG compressed domain, multimedia communication, multimedia information, objective evaluation scheme, variable bandwidth, video coding, video communication, video delivery, video sequences},
	doi = {10.1109/ICDT.2006.26},
	author = {Doulamis, A.D. and D. Kosmopoulos and Doulamis, N.D.}
}
