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IJCNN
2006
IEEE
13 years 9 months ago
Non-Relevance Feedback Document Retrieval based on One Class SVM and SVDD
— This paper reports a new document retrieval method using non-relevant documents. Especially, this paper reports a comparison of retrieval efficiency between One Class Support ...
Takashi Onoda, Hiroshi Murata, Seiji Yamada
ICMCS
2005
IEEE
221views Multimedia» more  ICMCS 2005»
13 years 9 months ago
A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on On...
Chengcui Zhang, Xin Chen, Min Chen, Shu-Ching Chen...
CIVR
2003
Springer
126views Image Analysis» more  CIVR 2003»
13 years 8 months ago
Learning in Region-Based Image Retrieval
In this paper, several effective learning algorithms using global image representations are adjusted and introduced to region-based image retrieval (RBIR). First, the query point m...
Feng Jing, Mingjing Li, Lei Zhang, HongJiang Zhang...
MMS
2008
13 years 3 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
CIVR
2005
Springer
123views Image Analysis» more  CIVR 2005»
13 years 9 months ago
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
Chengcui Zhang, Xin Chen