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Bayesian network structure learning and inference in indoor vs. outdoor image classification
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
doi:10.1109/icpr.2004.1334268
dblp:conf/icpr/KaneS04
fatcat:2p7tselpzzg3vhmjvq7bjwadrm