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» Extending Relevance Model for Relevance Feedback
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CIKM
2007
Springer
15 years 3 months ago
Generalizing from relevance feedback using named entity wildcards
Traditional adaptive filtering systems learn the user’s interests in a rather simple way – words from relevant documents are favored in the query model, while words from irre...
Abhimanyu Lad, Yiming Yang
ICDE
2006
IEEE
191views Database» more  ICDE 2006»
15 years 11 months ago
Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval
Today's Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (kNN) model. They retrieve images from a single neighborhood using lo...
Kien A. Hua, Ning Yu, Danzhou Liu
ACIVS
2005
Springer
15 years 3 months ago
Interactive Object-Based Retrieval Using Relevance Feedback
In this paper we present an interactive, object-based video retrieval system which features a novel query formulation method that is used to iteratively refine an underlying model...
Sorin Sav, Hyowon Lee, Noel E. O'Connor, Alan F. S...
ICCV
2007
IEEE
15 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
96
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ICCV
2003
IEEE
15 years 2 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...