Solving MRF minimization by mirror descent

Duy V.N. Luong, Panos Parpas, Daniel Rueckert, Berç Rustem

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


Markov Random Fields (MRF) minimization is a well-known problem in computer vision. We consider the augmented dual of the MRF minimization problem and develop a Mirror Descent algorithm based on weighted Entropy and Euclidean Projection. The augmented dual problem consists of maximizing a non-differentiable objective function subject to simplex and linear constraints. We analyze the convergence properties of the algorithm and sharpen its convergence rate. In addition, we also use the convergence analysis to identify an optimal stepsize strategy for weighted entropy projection and an adaptive stepsize strategy for weighted Euclidean projection. Experimental results on synthetic and vision problems demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 8th International Symposium, ISVC 2012, Revised Selected Papers
Number of pages12
EditionPART 1
StatePublished - 2012
Externally publishedYes
Event8th International Symposium on Visual Computing, ISVC 2012 - Rethymnon, Crete, Greece
Duration: 16 Jul 201218 Jul 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7431 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Symposium on Visual Computing, ISVC 2012
CityRethymnon, Crete


Dive into the research topics of 'Solving MRF minimization by mirror descent'. Together they form a unique fingerprint.

Cite this