PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Global Connectivity Potentials for Random Field Models
Sebastian Nowozin and Christoph Lampert
In: IEEE Computer Vision and Pattern Recognition (CVPR), Miami, FL(2009).

Abstract

Markov random eld (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local inter- actions and cannot model global properties, such as con- nectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deriving a potential function that enforces the output labeling to be connected and that can naturally be used in the framework of recent MAP-MRF LP relaxations. Using techniques from polyhedral combinatorics, we show that a provably tight approximation to the MAP solution of the resulting MRF can still be found efciently by solving a sequence of max-ow problems. The efciency of the in- ference procedure also allows us to learn the parameters of a MRF with global connectivity potentials by means of a cutting plane algorithm. We experimentally evaluate our al- gorithm on both synthetic data and on the challenging seg- mentation task of the PASCAL VOC 2008 data set. We show that in both cases the addition of a connectedness prior sig- nicantly reduces the segmentation error.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6316
Deposited By:Christoph Lampert
Deposited On:08 March 2010