NDLI: Mining activities using sticky multimodal dual hierarchical Dirichlet process hidden Markov model

Content Provider	IEEE Xplore Digital Library
Author	Guodong Tian Chunfeng Yuan Weiming Hu Zhaoquan Cai
Copyright Year	2013
Description	Author affiliation: Inst. of Autom., Beijing, China (Guodong Tian; Chunfeng Yuan; Weiming Hu) \|\| Huizhou Univ., Huizhou, China (Zhaoquan Cai)
Abstract	In this paper, a new nonparametric Bayesian model called Sticky Multimodal Dual Hierarchical Dirichlet Process Hidden Markov Model (SMD-HDP-HMM) is proposed for mining activities from a collection of time series. An activity is modeled as an HMM where each state corresponds to an atomic activity. By extensively using Dirichlet Process (DP), multiple HMMs sharing a common set of states are learned and the numbers of HMMs and states are both automatically determined. Each time series is modeled to be generated by one of the HMMs such that all time series are clustered into activities. Simultaneously state sequences for time series are learned and each of them is decomposed into a sequence of atomic activities. Experimental results on KTH activity dataset demonstrate the advantage of our method.
Starting Page	98
Ending Page	102
File Size	744883
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Time series analysis Bayes methods Computational modeling Markov processes Computer vision Feature extraction time series Dirichlet process HMM HDP activity mining
Content Type	Text
Resource Type	Article

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