Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs

Nicolas Heess, Nicolas Le Roux, John Winn

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2011
Subtitle of host publication21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II
PublisherSpringer Berlin Heidelberg
Number of pages8
ISBN (Electronic)978-3-642-21738-8
ISBN (Print)978-3-642-21737-1
Publication statusPublished - 2011
EventInternational Conference on Artificial Neural Networks (ICANN) - Espoo, Finland
Duration: 14 Jun 201117 Jun 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


ConferenceInternational Conference on Artificial Neural Networks (ICANN)

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