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» Manifold-ranking based image retrieval
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MIR
2004
ACM
171views Multimedia» more  MIR 2004»
14 years 2 months ago
Mean version space: a new active learning method for content-based image retrieval
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
Jingrui He, Hanghang Tong, Mingjing Li, HongJiang ...
MIR
2010
ACM
207views Multimedia» more  MIR 2010»
13 years 7 months ago
Learning to rank for content-based image retrieval
In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking st...
Fabio F. Faria, Adriano Veloso, Humberto Mossri de...
MM
2004
ACM
167views Multimedia» more  MM 2004»
14 years 2 months ago
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...
Xiaofei He, Wei-Ying Ma, HongJiang Zhang
MM
2005
ACM
171views Multimedia» more  MM 2005»
14 years 2 months ago
Semantic manifold learning for image retrieval
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
MMS
2008
13 years 9 months ago
Semantic interactive image retrieval combining visual and conceptual content description
We address the challenge of semantic gap reduction for image retrieval through an improved SVM-based active relevance feedback framework, together with a hybrid visual and concept...
Marin Ferecatu, Nozha Boujemaa, Michel Crucianu