This study addresses the problem of learning the optimal structure of a dynamic graphical model for video content analysis given sparse data. We propose a Completed Likelihood AIC (CL-AIC) scoring function that differs from existing ones by optimising explicitly both the explanation and prediction capabilities of a model simultaneously. We demonstrate that CL-AIC is superior to existing scoring functions including BIC, AIC and ICL in building dynamic graph models for video content analysis.
['Tao Xiang', 'Shaogang Gong']
Optimal Dynamic Graphs for Video Content Analysis