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126
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TKDE
2008
195views more  TKDE 2008»
15 years 14 days 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
MIR
2004
ACM
171views Multimedia» more  MIR 2004»
15 years 6 months ago
Mean version space: a new active learning method for content-based image retrieval
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
Jingrui He, Hanghang Tong, Mingjing Li, HongJiang ...
IVC
2007
176views more  IVC 2007»
15 years 15 days ago
Kernel-based distance metric learning for content-based image retrieval
ct 8 For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system 9 depends critically on the similarity or diss...
Hong Chang, Dit-Yan Yeung
KDD
1995
ACM
129views Data Mining» more  KDD 1995»
15 years 4 months ago
Feature Extraction for Massive Data Mining
Techniques for learning from data typically require data to be in standard form. Measurements must be encoded in a numerical format such as binary true-or-false features, numerica...
V. Seshadri, Raguram Sasisekharan, Sholom M. Weiss
108
Voted
ICIP
2005
IEEE
16 years 2 months ago
Semantic kernel learning for interactive image retrieval
Content-based image retrieval systems still have difficulties to bridge the semantic gap between the low-level representation of images and the high level concepts the user is loo...
Philippe Henri Gosselin, Matthieu Cord