@inproceedings{42411f4dc41e4c8aa29ca53d6082ba6f,
title = "Sublabel-accurate convex relaxation of vectorial multilabel energies",
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.",
keywords = "Convex relaxation, Optimization, Variational methods",
author = "Emanuel Laude and Thomas M{\"o}llenhoff and Michael Moeller and Jan Lellmann and Daniel Cremers",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 14th European Conference on Computer Vision, ECCV 2016 ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46448-0_37",
language = "English",
isbn = "9783319464473",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "614--627",
editor = "Bastian Leibe and Jiri Matas and Nicu Sebe and Max Welling",
booktitle = "Computer Vision - 14th European Conference, ECCV 2016, Proceedings",
}