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Descriptor Learning Using Convex Optimisation

Abstract:

The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity; second, it is shown that dimensionality reduction can also be formulated as a convex optimisation problem, using the nuclear norm to reduce dimens...

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Publication status:
Published

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Journal:
COMPUTER VISION - ECCV 2012, PT I
Volume:
7572
Issue:
PART 1
Pages:
243-256
Publication date:
2012-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
URN:
uuid:7c1045cc-2e24-4ebb-9cdc-ce92ad13ca2d
Source identifiers:
360062
Local pid:
pubs:360062
Language:
English

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