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A Bayesian approach integrating regional and global features for image semantic learning
2009
2009 IEEE International Conference on Multimedia and Expo
In content-based image retrieval, the "semantic gap" between visual image features and user semantics makes it hard to predict abstract image categories from low-level features. We present a hybrid system that integrates global features (Gfeatures) and region features (R-features) for predicting image semantics. As an intermediary between image features and categories, we introduce the notion of mid-level concepts, which enables us to predict an image's category in three steps. First, a
doi:10.1109/icme.2009.5202554
dblp:conf/icmcs/NguyenYLTCL09
fatcat:hj5aqunen5df5ee4vbhy5zj6jq