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ICCV
2007
IEEE
14 years 11 months ago
Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval
Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of quest...
Hichem Sahbi, Jean-Yves Audibert, Renaud Keriven
CIKM
2009
Springer
14 years 4 months ago
Exploring relevance for clicks
Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algor...
Rongwei Cen, Yiqun Liu, Min Zhang, Bo Zhou, Liyun ...
ICCV
2003
IEEE
14 years 3 months ago
Reinforcement Learning for Combining Relevance Feedback Techniques
Relevance feedback (RF) is an interactive process which refines the retrievals by utilizing user’s feedback history. Most researchers strive to develop new RF techniques and ign...
Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei...
COGSCI
2010
93views more  COGSCI 2010»
13 years 10 months ago
The Evolution of Relevance
With human language, the same utterance can have different meanings in different contexts. Nevertheless, listeners almost invariably converge upon the correct intended meaning. Th...
Thomas C. Scott-Phillips
ADCS
2004
13 years 11 months ago
On the Effectiveness of Relevance Profiling
Relevance profiling is a general process for withindocument retrieval. Given a query, a profile of retrieval status values is computed by sliding a fixed sized window across a doc...
David J. Harper, David Lee