Sciweavers

Share
ICCV
2009
IEEE

A Global Perspective on MAP Inference for Low-Level Vision

12 years 11 months ago
A Global Perspective on MAP Inference for Low-Level Vision
In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, MRFs inherently encourage delta function marginal statistics. By contrast, many low-level vision problems have heavy tailed marginal statistics, making the MRF model unsuitable. In this paper we introduce a more general Marginal Probability Field (MPF), of which the MRF is a special, linear case, and show that convex energy MPFs can be used to encourage arbitrary marginal statistics. We introduce a flexible, extensible framework for effectively optimizing the resulting NP-hard MAP problem, based around dual-decomposition and a modified mincost flow algorithm, and which achieves global optimality in some instances. We use a range of applications, including image denoising and texture synthesis, to demonstrate the benefits of this class of MPF over MRFs.
Oliver J. Woodford, Carsten Rother, Vladimir Kolmo
Added 13 Jul 2009
Updated 10 Jan 2010
Type Conference
Year 2009
Where ICCV
Authors Oliver J. Woodford, Carsten Rother, Vladimir Kolmogorov
Comments (0)
books