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

Submitted by Anonymous (not verified) on Wed, 11/28/2012 - 18:57
TitleVisual Workflow Recognition Using a Variational Bayesian Treatment of Multistream Fused Hidden Markov Models
Publication TypeJournal Article
Year of Publication2012
AuthorsChatzis, SP, Kosmopoulos, D
JournalCircuits and Systems for Video Technology, IEEE Transactions on
Volume22
Issue7
Pagination1076 -1086
Date PublishedJuly
ISSN1051-8215
Keywordsactive 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
DOI10.1109/TCSVT.2012.2189795
PDF icon ieee-t-csvt12.pdf