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2008

Optimising dynamic graphical models for video content analysis

8 years 10 months ago
Optimising dynamic graphical models for video content analysis
A key problem in video content analysis using dynamic graphical models is to learn a suitable model structure given some observed visual data. We propose a Completed Likelihood AIC (CL-AIC) scoring function for solving the problem. CL-AIC differs from existing scoring functions in that it aims to optimise explicitly both the explanation and prediction capabilities of a model simultaneously. CL-AIC is derived as a general scoring function suitable for both static and dynamic graphical models with hidden variables. In particular, we formulate CL-AIC for determining the number of hidden states for a Hidden Markov Model (HMM) and the topology of a Dynamically Multi-Linked HMM (DML-HMM). The effectiveness of CL-AIC on learning the optimal structure of a dynamic graphical model especially given sparse and noisy visual date is shown through comparative experiments against existing scoring functions including Bayesian Information Criterion (BIC), Akaike's Information Criterion (AIC), Int...
Tao Xiang, Shaogang Gong
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where CVIU
Authors Tao Xiang, Shaogang Gong
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