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Located Hidden Random Fields: Learning Discriminative Parts for Object Detection

  • Ashish Kapoor
  • John Winn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

This paper introduces the Located Hidden Random Field (LHRF), a conditional model for simultaneous part-based detection and segmentation of objects of a given class. Given a training set of images with segmentation masks for the object of interest, the LHRF automatically learns a set of parts that are both discriminative in terms of appearance and informative about the location of the object. By introducing the global position of the object as a latent variable, the LHRF models the long-range spatial configuration of these parts, as well as their local interactions. Experiments on benchmark datasets show that the use of discriminative parts leads to state-of-the-art detection and segmentation performance, with the additional benefit of obtaining a labeling of the object’s component parts.

Keywords

Object Detection Object Class Conditional Random Field Discriminative Model Segmentation Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ashish Kapoor
    • 1
  • John Winn
    • 2
  1. 1.MIT Media LaboratoryCambridgeUSA
  2. 2.Microsoft ResearchCambridgeUK

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