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Mapping Low-Level Features to High-Level Semantic Concepts in Region-Based Image Retrieval

9 years 4 months ago
Mapping Low-Level Features to High-Level Semantic Concepts in Region-Based Image Retrieval
In this a novel supervised learning method is proposed to map low-level visualfeatures to high-level semantic conceptsfor region-based image retrieval. The contributions of thispaper lie in threefolds. ( I ) For each semantic concept, a set of low-level tokens are extracted fmm the segmented regions of training images. Those tokens capture the representative informationfor describing the semantic meaning of that concept; (2)A set of posteriors are generated based on the low-level tokens through which denote the probabilities of images belonging to the semantic concepts. Theposteriors are treated as high-levelfeatures that connect images with high-level semantic concepts. Long-term relevance feedback learning is incorporated to provide the supervisory information needed in the above learning process, including the concept informationand the relevanttraining setfor each concept; (3)An integratedalgorithm is implemented to combine two kinds of informationfor retrieval: the informationfro...
Wei Jiang, Kap Luk Chan, Mingjing Li, HongJiang Zh
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CVPR
Authors Wei Jiang, Kap Luk Chan, Mingjing Li, HongJiang Zhang
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