Bayesian network structure learning and inference in indoor vs. outdoor image classification

M.J. Kane, A. Savakis
2004 Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.  
Bayesian network model selection techniques may be used to learn and elucidate conditional relationships between features in pattern recognition tasks. The learned Bayesian network may then be used to infer unknown nodestates, which may correspond to semantic tasks. One such application of this framework is scene categorization. In this paper, we employ low-level classification based on color and texture, semantic features, such as sky and grass detection, along with indoor vs. outdoor ground
more » ... uth information, to create a feature set for Bayesian network structure learning. Indoor vs. outdoor inference may then be performed on a set of features derived from a testing set where node states are unknown. Experimental results show that this technique provides classification rates of 97% correct, which is a significant improvement over previous work, where a Bayesian network was constructed based on expert opinion.
doi:10.1109/icpr.2004.1334268 dblp:conf/icpr/KaneS04 fatcat:2p7tselpzzg3vhmjvq7bjwadrm