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» Relevance Feedback in Content-based Image Search
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ICCV
2011
IEEE
12 years 7 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
JMLR
2010
127views more  JMLR 2010»
13 years 2 months ago
Content-based Image Retrieval with Multinomial Relevance Feedback
The paper considers an interactive search paradigm in which at each round a user is presented with a set of k images and is required to select one that is closest to her target. P...
Dorota Glowacka, John Shawe-Taylor
TKDE
2008
195views more  TKDE 2008»
13 years 7 months ago
Learning a Maximum Margin Subspace for Image Retrieval
One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap...
Xiaofei He, Deng Cai, Jiawei Han
MM
2006
ACM
164views Multimedia» more  MM 2006»
14 years 1 months ago
Scalable relevance feedback using click-through data for web image retrieval
Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalabil...
En Cheng, Feng Jing, Lei Zhang, Hai Jin
ICASSP
2011
IEEE
12 years 11 months ago
Topic-sensitive interactive image object retrieval with noise-proof relevance feedback
One current direction to enhance the search accuracy in visual object retrieval is to reformulate the original query through (pseudo-)relevance feedback, which augments a query wi...
Jen-Hao Hsiao, Henry Chang