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» Textural Features and Relevance Feedback for Image Retrieval
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MM
2004
ACM
152views Multimedia» more  MM 2004»
15 years 5 months ago
Manifold-ranking based image retrieval
In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ra...
Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang...
ICCV
2011
IEEE
13 years 11 months ago
HEAT: Iterative Relevance Feedback with One Million Images
It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hu...
Nicolae Suditu, Francois Fleuret
CIVR
2008
Springer
127views Image Analysis» more  CIVR 2008»
15 years 1 months ago
Performance evaluation of relevance feedback methods
In this paper we review the evaluation of relevance feedback methods for content-based image retrieval systems. We start out by presenting an overview of current common practice, ...
Mark J. Huiskes, Michael S. Lew
DICTA
2008
15 years 1 months ago
Digital Image Retrieval Using Intermediate Semantic Features and Multistep Search
Recently, semantic image retrieval has attracted large amount of interest due to the rapid growth of digital image storage. However, existing approaches have severe limitations. I...
Dengsheng Zhang, Ying Liu, Jin Hou
IDEAL
2005
Springer
15 years 5 months ago
Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval
It is known that no single descriptor is powerful enough to encompass all aspects of image content, i.e. each feature extraction method has its own view of the image content. A pos...
Lin Mei, Gerd Brunner, Lokesh Setia, Hans Burkhard...