Optimal Dynamic Graphs for Video Content Analysis

Abstract

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.

Optimising dynamic graphical models for video content analysis

“…assumed) [18]. However, much less effort has been made to tackle the more challenging problem of learning the optimal structure of an unknown DBN [4,17,11,35]. Most previous DBNs-based video content modelling approaches avoid the structure learning problem by setting the structure manually [21,26,8,15].…”