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» Optimizing Learning in Image Retrieval
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CIKM
2010
Springer
13 years 8 months ago
Learning to rank relevant and novel documents through user feedback
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Abhimanyu Lad, Yiming Yang
EMMCVPR
2005
Springer
14 years 3 months ago
Reverse-Convex Programming for Sparse Image Codes
Abstract. Reverse-convex programming (RCP) concerns global optimization of a specific class of non-convex optimization problems. We show that a recently proposed model for sparse ...
Matthias Heiler, Christoph Schnörr
ICPR
2004
IEEE
14 years 11 months ago
Object Class Recognition using Images of Abstract Regions
lass Recognition using Images of Abstract Regions Yi Li, Jeff A. Bilmes, and Linda G. Shapiro Department of Computer Science and Engineering Department of Electrical Engineering Un...
Jeff A. Bilmes, Linda G. Shapiro, Yi Li
ICML
2009
IEEE
14 years 11 months ago
BoltzRank: learning to maximize expected ranking gain
Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
Maksims Volkovs, Richard S. Zemel
ICASSP
2009
IEEE
14 years 5 months ago
Annotating images by harnessing worldwide user-tagged photos
Automatic image tagging is important yet challenging due to the semantic gap and the lack of learning examples to model a tag’s visual diversity. Meanwhile, social user tagging ...
Xirong Li, Cees G. M. Snoek, Marcel Worring