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» Learning in Region-Based Image Retrieval
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MIR
2004
ACM
236views Multimedia» more  MIR 2004»
14 years 2 months ago
Boosting contextual information in content-based image retrieval
We present a new framework for characterizing and retrieving objects in cluttered scenes. This CBIR system is based on a new representation describing every object taking into acc...
Jaume Amores, Nicu Sebe, Petia Radeva, Theo Gevers...
MMS
2008
13 years 8 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
MM
2009
ACM
209views Multimedia» more  MM 2009»
14 years 3 months ago
Brain state decoding for rapid image retrieval
Human visual perception is able to recognize a wide range of targets under challenging conditions, but has limited throughput. Machine vision and automatic content analytics can p...
Jun Wang, Eric Pohlmeyer, Barbara Hanna, Yu-Gang J...
MIR
2005
ACM
137views Multimedia» more  MIR 2005»
14 years 2 months ago
A content-based image retrieval system for fish taxonomy
It is estimated that less than ten percent of the world’s species have been discovered and described. The main reason for the slow pace of new species description is that the sc...
Yixin Chen, Henry L. Bart Jr., Fei Teng
ICASSP
2007
IEEE
14 years 3 months ago
Integrating Relevance Feedback in Boosting for Content-Based Image Retrieval
Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In thi...
Jie Yu, Yijuan Lu, Yuning Xu, Nicu Sebe, Qi Tian