MRF energy minimization and beyond via dual decomposition

This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1998. - 33(2011), 3 vom: 17. März, Seite 531-52
1. Verfasser: Komodakis, Nikos (VerfasserIn)
Weitere Verfasser: Paragios, Nikos, Tziritas, Georgios
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2011
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Journal Article
LEADER 01000caa a22002652 4500
001 NLM198177712
003 DE-627
005 20250211140003.0
007 cr uuu---uuuuu
008 231223s2011 xx |||||o 00| ||eng c
024 7 |a 10.1109/TPAMI.2010.108  |2 doi 
028 5 2 |a pubmed25n0661.xml 
035 |a (DE-627)NLM198177712 
035 |a (NLM)20479493 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Komodakis, Nikos  |e verfasserin  |4 aut 
245 1 0 |a MRF energy minimization and beyond via dual decomposition 
264 1 |c 2011 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 23.06.2011 
500 |a Date Revised 21.04.2011 
500 |a published: Print 
500 |a Citation Status MEDLINE 
520 |a This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems, and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and demonstrate the extreme generality and flexibility of such an approach. We thus show that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, and 3) take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g., graph-cut-based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach 
650 4 |a Journal Article 
700 1 |a Paragios, Nikos  |e verfasserin  |4 aut 
700 1 |a Tziritas, Georgios  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on pattern analysis and machine intelligence  |d 1998  |g 33(2011), 3 vom: 17. März, Seite 531-52  |w (DE-627)NLM098212257  |x 1939-3539  |7 nnns 
773 1 8 |g volume:33  |g year:2011  |g number:3  |g day:17  |g month:03  |g pages:531-52 
856 4 0 |u http://dx.doi.org/10.1109/TPAMI.2010.108  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 33  |j 2011  |e 3  |b 17  |c 03  |h 531-52