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CVPR
2005
IEEE
14 years 8 months ago
A Semi-Supervised Active Learning Framework for Image Retrieval
Although recent studies have shown that unlabeled data are beneficial to boosting the image retrieval performance, very few approaches for image retrieval can learn with labeled a...
Steven C. H. Hoi, Michael R. Lyu
MIR
2010
ACM
207views Multimedia» more  MIR 2010»
13 years 4 months ago
Learning to rank for content-based image retrieval
In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking st...
Fabio F. Faria, Adriano Veloso, Humberto Mossri de...
ICIP
2002
IEEE
14 years 7 months ago
Content-based image retrieval for digital mammography
In this work, we explore the use of a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. A fundamental issue is...
Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Niko...
ECCV
2000
Springer
14 years 8 months ago
Learning Similarity for Texture Image Retrieval
A novel algorithm is proposed to learn pattern similarities for texture image retrieval. Similar patterns in di erent texture classes are grouped into a cluster in the feature spac...
Guodong Guo, Stan Z. Li, Kap Luk Chan
ISBI
2009
IEEE
14 years 1 months ago
Bridging the Semantic Gap Using Ranking Svm for Image Retrieval
One of the main challenges for Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings between the high-level semantic concepts and the low-level visual features in...
Haiying Guan, Sameer Antani, L. Rodney Long, Georg...