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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
ICTIR
2009
Springer
14 years 2 months ago
"A term is known by the company it keeps": On Selecting a Good Expansion Set in Pseudo-Relevance Feedback
Abstract. It is well known that pseudo-relevance feedback (PRF) improves the retrieval performance of Information Retrieval (IR) systems in general. However, a recent study by Cao ...
Raghavendra Udupa, Abhijit Bhole, Pushpak Bhattach...
MM
2009
ACM
185views Multimedia» more  MM 2009»
14 years 2 months ago
Deep exploration for experiential image retrieval
Experiential image retrieval systems aim to provide the user with a natural and intuitive search experience. The goal is to empower the user to navigate large collections based on...
Bart Thomee, Mark J. Huiskes, Erwin M. Bakker, Mic...
MMS
2007
13 years 7 months ago
Evaluating a workspace's usefulness for image retrieval
Image searching is a creative process. We have proposed a novel image retrieval system that supports creative search sessions by allowing the user to organise their search results ...
Jana Urban, Joemon M. Jose
ICITA
2005
IEEE
14 years 1 months ago
Combining Diversity-Based Active Learning with Discriminant Analysis in Image Retrieval
Small-sample learning in image retrieval is a pertinent and interesting problem. Relevance feedback is an active area of research that seeks to find algorithms that are robust wi...
Charlie K. Dagli, ShyamSundar Rajaram, Thomas S. H...