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» Learning the Relative Importance of Features in Image Data
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CVPR
2008
IEEE
16 years 7 months ago
Transfer learning for image classification with sparse prototype representations
To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer le...
Ariadna Quattoni, Michael Collins, Trevor Darrell
149
Voted
JMM2
2006
111views more  JMM2 2006»
15 years 5 months ago
A Microscopic Telepathology System for Multiresolution Computer-Aided Diagnostics
The aim of the presented system is simplification and speedup of the daily pathological examination routine. The system combines telepathology with computer-aided diagnostics algor...
Grigory Begelman, Michael Pechuk, Ehud Rivlin, Edm...
CVPR
2008
IEEE
16 years 7 months ago
Fast image search for learned metrics
We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis d...
Prateek Jain, Brian Kulis, Kristen Grauman
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
16 years 13 hour ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
135
Voted
SG
2007
Springer
15 years 11 months ago
Data-Embeddable Texture Synthesis
Abstract. Data hiding techniques onto images provide tools for protecting copyright or sending secret messages, and they are currently utilized as a simple input device of a cell p...
Hirofumi Otori, Shigeru Kuriyama