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» Learning to rank for content-based image retrieval
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IJCV
2007
163views more  IJCV 2007»
10 years 4 months ago
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
In this paper, we present a novel image representation that renders it possible to access natural scenes by local semantic description. Our work is motivated by the continuing effo...
Julia Vogel, Bernt Schiele
CLEF
2005
Springer
10 years 9 months ago
FIRE in ImageCLEF 2005: Combining Content-Based Image Retrieval with Textual Information Retrieval
In this paper the methods we used in the 2005 ImageCLEF content-based image retrieval evaluation are described. For the medical retrieval task, we combined several low-level image ...
Thomas Deselaers, Tobias Weyand, Daniel Keysers, W...
ISBI
2009
IEEE
10 years 11 months ago
Bridging the Semantic Gap Using Ranking Svm for Image Retrieval
One of the main challenges for Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings between the high-level semantic concepts and the low-level visual features in...
Haiying Guan, Sameer Antani, L. Rodney Long, Georg...
CLEF
2005
Springer
10 years 9 months ago
Content-Based Retrieval of Medical Images by Combining Global Features
A combination of several classi´Čüers using global features for the content description of medical images is proposed. Beside well known texture histogram features, downscaled repr...
Mark Oliver Güld, Christian Thies, Benedikt F...
CISST
2004
164views Hardware» more  CISST 2004»
10 years 5 months ago
Probabilistic Region Relevance Learning for Content-Based Image Retrieval
Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
Iker Gondra, Douglas R. Heisterkamp
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