Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification

  • Authors:
  • Sanjiv Kumar;Martial Hebert

  • Affiliations:
  • -;-

  • Venue:
  • ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
  • Year:
  • 2003

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Abstract

In this work we present Discriminative Random Fields(DRFs), a discriminative framework for the classification ofimage regions by incorporating neighborhood interactionsin the labels as well as the observed data. The discriminativerandom fields offer several advantages over the conventionalMarkov Random Field (MRF) framework. First,the DRFs allow to relax the strong assumption of conditionalindependence of the observed data generally used inthe MRF framework for tractability. This assumption is toorestrictive for a large number of applications in vision. Second,the DRFs derive their classification power by exploitingthe probabilistic discriminative models instead of thegenerative models used in the MRF framework. Finally, allthe parameters in the DRF model are estimated simultaneouslyfrom the training data unlike the MRF frameworkwhere likelihood parameters are usually learned separatelyfrom the field parameters. We illustrate the advantages ofthe DRFs over the MRF framework in an application ofman-made structure detection in natural images taken fromthe Corel database.