Visual Learning Given Sparse Data of Unknown Complexity

Abstract

This study addresses the problem of unsupervised visual learning. It examines existing popular model order selection criteria before proposes two novel criteria for improving visual learning given sparse data and without any knowledge about model complexity. In particular, a rectified Bayesian information criterion (BICr) and a completed likelihood Akaike's information criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for learning the dynamic structure of a visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from very small to large. Extensive experiments on learning a dynamic scene structure are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of BIC (Schwarz, 1978), AIC (Akaike, 1973), ICL (Biernacki, 2000) and a MML (Figueiredo and Jain, 2002) based criterion.