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A Constrained Semi−supervised Learning Approach to Data Association

Abstract:

Data association (obtaining correspondences) is a ubiquitous problem in computer vision. It appears when matching image features across multiple images, matching image features to object recognition models and matching image features to semantic concepts. In this paper, we show how a wide class of data association tasks arising in computer vision can be interpreted as a constrained semi-supervised learning problem. This interpretation opens up room for the development of new, more efficient d...

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Publisher copy:
10.1007/978-3-540-24672-5_1
Publisher:
Springer Berlin Heidelberg
Host title:
Computer Vision − ECCV 2004
Volume:
3023
Publication date:
2004-01-01
DOI:
ISBN:
9783540219828
UUID:
uuid:f9351655-bf7b-4820-bb53-ca12d66e41d6
Local pid:
cs:7524
Deposit date:
2015-03-31

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