A Multiscale Variable-Grouping Framework for MRF Energy Minimization

Autor:	Meirav Galun, Stav Yagev, Omer Meir, Irad Yavneh, Ronen Basri
Rok vydání:	2015
Předmět:	Mathematical optimization Iterative and incremental development Random field Scale (ratio) Markov chain Pairwise comparison Graphical model Energy minimization Energy (signal processing) Mathematics
Zdroj:	ICCV
DOI:	10.1109/iccv.2015.210
Popis:	We present a multiscale approach for minimizing the energy associated with Markov Random Fields (MRFs) with energy functions that include arbitrary pairwise potentials. The MRF is represented on a hierarchy of successively coarser scales, where the problem on each scale is itself an MRF with suitably defined potentials. These representations are used to construct an efficient multiscale algorithm that seeks a minimal-energy solution to the original problem. The algorithm is iterative and features a bidirectional crosstalk between fine and coarse representations. We use consistency criteria to guarantee that the energy is nonincreasing throughout the iterative process. The algorithm is evaluated on real-world datasets, achieving competitive performance in relatively short run-times.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::87652c0ca3d096aaef0c0a6573a9150b https://doi.org/10.1109/iccv.2015.210 Zobrazit plný text záznamu