caa a22 4500 467914389 CHVBK 20180406152858.0 cr unu---uuuuu 170328e20060801xx s 000 0 eng 10.1007/s11263-005-5024-8 doi (NATIONALLICENCE)springer-10.1007/s11263-005-5024-8 Model Selection for Unsupervised Learning of Visual Context [Elektronische Daten] [Tao Xiang, Shaogang Gong] This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. 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 a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes. Springer Science + Business Media, LLC, 2006 learning for vision nationallicence visual context nationallicence model selection nationallicence dynamic scene modelling nationallicence clustering nationallicence Bayesian methods nationallicence mixture models nationallicence Xiang Tao Department of Computer Science, Queen Mary, University of London, E1 4NS, London, UK aut Gong Shaogang Department of Computer Science, Queen Mary, University of London, E1 4NS, London, UK aut International Journal of Computer Vision Kluwer Academic Publishers 69/2(2006-08-01), 181-201 0920-5691 69:2<181 2006 69 11263 https://doi.org/10.1007/s11263-005-5024-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-005-5024-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Xiang Tao Department of Computer Science, Queen Mary, University of London, E1 4NS, London, UK aut NATIONALLICENCE 700 1- Gong Shaogang Department of Computer Science, Queen Mary, University of London, E1 4NS, London, UK aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 69/2(2006-08-01), 181-201 0920-5691 69:2<181 2006 69 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer