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CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

Authors : Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Jan Lellmann, Daniel Cremers

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

Convex relaxations of multilabel problems have been demonstrated to produce provably optimal or near-optimal solutions to a variety of computer vision problems. Yet, they are of limited practical use as they require a fine discretization of the label space, entailing a huge demand in memory and runtime. In this work, we propose the first sublabel accurate convex relaxation for vectorial multilabel problems. Our key idea is to approximate the dataterm in a piecewise convex (rather than piecewise linear) manner. As a result we have a more faithful approximation of the original cost function that provides a meaningful interpretation for fractional solutions of the relaxed convex problem.

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Appendix
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Footnotes
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Metadata
Title
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies
Authors
Emanuel Laude
Thomas Möllenhoff
Michael Moeller
Jan Lellmann
Daniel Cremers
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46448-0_37

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