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Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs

  • Nicolas Heess
  • Nicolas Le Roux
  • John Winn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6792)

Abstract

We propose an extension of the Restricted Boltzmann Machine (RBM) that allows the joint shape and appearance of foreground objects in cluttered images to be modeled independently of the background. We present a learning scheme that learns this representation directly from cluttered images with only very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foreground objects independently of the background can be beneficial in recognition tasks.

Keywords

RBM segmentation weakly supervised learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nicolas Heess
    • 1
  • Nicolas Le Roux
    • 2
  • John Winn
    • 3
  1. 1.IANCUniversity of EdinburghEdinburghUK
  2. 2.Sierra TeamINRIAParisFrance
  3. 3.Microsoft ResearchCambridgeUK

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