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» Optimizing Learning in Image Retrieval
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CVPR
2008
IEEE
14 years 9 months ago
Multiple-instance ranking: Learning to rank images for image retrieval
We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training e...
Yang Hu, Mingjing Li, Nenghai Yu
PKDD
2009
Springer
162views Data Mining» more  PKDD 2009»
14 years 2 months ago
A Convex Method for Locating Regions of Interest with Multi-instance Learning
Abstract. In content-based image retrieval (CBIR) and image screening, it is often desirable to locate the regions of interest (ROI) in the images automatically. This can be accomp...
Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua ...
AAAI
2008
13 years 10 months ago
Instance-level Semisupervised Multiple Instance Learning
Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-...
Yangqing Jia, Changshui Zhang
CVPR
2005
IEEE
14 years 9 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
IEEEMSP
2002
IEEE
167views Multimedia» more  IEEEMSP 2002»
14 years 17 days ago
An experimental study on the performance of visual information retrieval similarity models
–This paper is an experimental study on the performance of the two major methods for macro-level similarity measurement: linear weighted merging and logical retrieval. Performanc...
Horst Eidenberger, Christian Breiteneder