Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs

Nicolas Heess; Nicolas Le Roux; John Winn

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arXiv · 1107.3823

Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs

Nicolas Heess·Nicolas Le Roux·John Winn

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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.

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Nicolas Heess, Nicolas Le Roux, John Winn. 2011-07-19. Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs. https://arxiv.org/abs/1107.3823

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