Discriminative random fields: a discriminative framework for contextual interaction in classification

Sanjiv Kumar Sanjiv Kumar,Hebert Hebert

doi:10.1109/iccv.2003.1238478

Abstract

In this work we present discriminative random fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov random field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used in the MRF framework. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where likelihood parameters are usually learned separately from the field parameters. We illustrate the advantages of the DRFs over the MRF framework in an application of man-made structure detection in natural images taken from the Corel database.

Highlights

The problem of region classification, i.e. segmentation and labeling of image regions is of fundamental interest in computer vision
In this work we present a new model called Discriminative Random Field based on the concept of Conditional Random Field (CRF) proposed by Lafferty et al [14] in the context of segmentation and labeling of the 1-D text sequences
We have proposed discriminative random fields for the classification of image regions while allowing neighborhood interactions in the labels as well as the observed data without making any model approximations

Summary

Introduction

The problem of region classification, i.e. segmentation and labeling of image regions is of fundamental interest in computer vision. Markov Random Field (MRF) models have been used extensively for various segmentation and labeling applications in vision, which allow one to incorporate contextual constraints in a principled manner [15]. MRFs are generally used in a probabilistic generative framework that models the joint probability of the observed data and the corresponding labels. Let y be the observed data from an input image, where y = {yi}i∈S, yi is the data from the ith site, and S is the set of sites. Let the corresponding labels at the image sites be given by x = {xi}i∈S. In the MRF framework, the posterior over the labels given the data is expressed using the Bayes’ rule as,

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