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...

• Type: Journal
• Year: 2008
• Where: CVIU
• Authors: Tao Xiang, Shaogang Gong
• Keywords: CVIU 2008, Dynamic Graphical Models, Scoring Functions, Video Content Analysis