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Modeling the joint density of two images under a variety of transformations

8 years 2 months ago
Modeling the joint density of two images under a variety of transformations
We describe a generative model of the relationship between two images. The model is defined as a factored threeway Boltzmann machine, in which hidden variables collaborate to define the joint correlation matrix for image pairs. Modeling the joint distribution over pairs makes it possible to efficiently match images that are the same according to a learned measure of similarity. We apply the model to several face matching tasks, and show that it learns to represent the input images using task-specific basis functions. Matching performance is superior to previous similar generative models, including recent conditional models of transformations. We also show that the model can be used as a plug-in matching score to perform invariant classification.
Joshua Susskind, Roland Memisevic, Geoffrey Hinton
Added 08 Apr 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Joshua Susskind, Roland Memisevic, Geoffrey Hinton, Marc Pollefeys
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