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» Learning to rank for content-based image retrieval
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CVPR
2001
IEEE
14 years 9 months ago
Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback
A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two parts. Im...
Guodong Guo, Anil K. Jain, Wei-Ying Ma, HongJiang ...

Publication
1763views
14 years 3 months ago
Reranking with Contextual dissimilarity measures from representational Bregman k-means
We present a novel reranking framework for Content Based Image Retrieval (CBIR) systems based on con-textual dissimilarity measures. Our work revisit and extend the method of Perro...
Olivier Schwander, Frank Nielsen
CLEF
2007
Springer
14 years 1 months ago
DCU and UTA at ImageCLEFPhoto 2007
Dublin City University (DCU) and University of Tampere (UTA) participated in the ImageCLEF 2007 photographic ad-hoc retrieval task with several monolingual and bilingual runs. Our...
Anni Järvelin, Peter Wilkins, Tomasz Adamek, ...
CVPR
2000
IEEE
14 years 9 months ago
Optimizing Learning in Image Retrieval
Combining learning with vision techniques in interactive image retrieval has been an active research topic during the past few years. However, existing learning techniques either ...
Yong Rui, Thomas S. Huang
ICPR
2008
IEEE
14 years 8 months ago
Learning weighted distances for relevance feedback in image retrieval
We present a new method for relevance feedback in image retrieval and a scheme to learn weighted distances which can be used in combination with different relevance feedback metho...
Enrique Vidal, Hermann Ney, Roberto Paredes, Thoma...