PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exact Inference in Multi-label CRFs with Higher Order Cliques
Srikumar Ramalingam, Pushmeet Kohli, Karteek Alahari and Philip Torr
In: CVPR, Alaska, USA(2008).


This paper addresses the problem of exactly inferring the maximum a posteriori solutions of discrete multi-label MRFs or CRFs with higher order cliques. We present a framework to transform special classes of multi-label higher order functions to submodular second order boolean functions (referred to as ${\cal F}_s^2$), which can be minimized exactly using graph cuts and we characterize those classes. The basic idea is to use two or more boolean variables to encode the states of a single multi-label variable. There are many ways in which this can be done and much interesting research lies in finding ways which are optimal or minimal in some sense. We study the space of possible encodings and find the ones that can transform the most general class of functions to ${\cal F}_s^2$. Our main contributions are two-fold. First, we extend the subclass of submodular energy functions that can be minimized exactly using graph cuts. Second, we show how higher order potentials can be used to improve single view 3D reconstruction results. We believe that our work on exact minimization of higher order energy functions will lead to similar improvements in solutions of other labelling problems.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4212
Deposited By:Karteek Alahari
Deposited On:27 November 2008