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Fusion Moves for Markov Random Field Optimization
August 2010 (vol. 32 no. 8)
pp. 1392-1405
ASCII Text
x 
 
Victor Lempitsky, Carsten Rother, Stefan Roth, Andrew Blake, "Fusion Moves for Markov Random Field Optimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1392-1405, August, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.143,
author = {Victor Lempitsky and Carsten Rother and Stefan Roth and Andrew Blake},
title = {Fusion Moves for Markov Random Field Optimization},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {8},
issn = {0162-8828},
year = {2010},
pages = {1392-1405},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.143},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Fusion Moves for Markov Random Field Optimization
IS - 8
SN - 0162-8828
SP1392
EP1405
EPD - 1392-1405
A1 - Victor Lempitsky,
A1 - Carsten Rother,
A1 - Stefan Roth,
A1 - Andrew Blake,
PY - 2010
KW - Markov random fields
KW - computer vision
KW - combinatorial algorithms
KW - graph algorithms
KW - stereo
KW - motion
KW - image restoration.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Victor Lempitsky, Microsoft Research, Cambridge
Carsten Rother, Micorsoft Research, Cambridge
Stefan Roth, Technische Universität Darmstadt, Darmstadt
Andrew Blake, Microsoft Research, Cambridge
The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph-cut-based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph-cut approaches, which allows them to be used as building blocks within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads, 2) for fast MRF optimization by combining cheap-to-compute solutions, and 3) for the optimization of highly nonconvex continuous-labeled MRFs with 2D labels. Our final example is a nonvision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation).

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Index Terms:
Markov random fields, computer vision, combinatorial algorithms, graph algorithms, stereo, motion, image restoration.
Citation:
Victor Lempitsky, Carsten Rother, Stefan Roth, Andrew Blake, "Fusion Moves for Markov Random Field Optimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1392-1405, Aug. 2010, doi:10.1109/TPAMI.2009.143
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