KAUST Grant NumberKUK-I1- 007-43
Online Publication Date2013-04-11
Print Publication Date2013-09
Permanent link to this recordhttp://hdl.handle.net/10754/597994
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AbstractRecently, variational relaxation techniques for approximating solutions of partitioning problems on continuous image domains have received considerable attention, since they introduce significantly less artifacts than established graph cut-based techniques. This work is concerned with the sources of such artifacts. We discuss the importance of differentiating between artifacts caused by discretization and those caused by relaxation and provide supporting numerical examples. Moreover, we consider in depth the consequences of a recent theoretical result concerning the optimality of solutions obtained using a particular relaxation method. Since the employed regularizer is quite tight, the considered relaxation generally involves a large computational cost. We propose a method to significantly reduce these costs in a fully automatic way for a large class of metrics including tree metrics, thus generalizing a method recently proposed by Strekalovskiy and Cremers (IEEE conference on computer vision and pattern recognition, pp. 1905-1911, 2011). © 2013 Springer Science+Business Media New York.
CitationLellmann J, Lellmann B, Widmann F, Schnörr C (2013) Discrete and Continuous Models for Partitioning Problems. Int J Comput Vis 104: 241–269. Available: http://dx.doi.org/10.1007/s11263-013-0621-4.
SponsorsThe second and third author were supported by Engineering and Physical Sciences Research Council (EPSRC)-Project EP/H016317/1. This publication is partly based on work supported by Award No. KUK-I1- 007-43, made by King Abdullah University of Science and Technology (KAUST).