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Conference Papers Year : 2011

Multivariate dictionary learning and shift & 2D rotation invariant sparse coding

Abstract

In this article, we present a new tool for sparse coding : Multivariate DLA which empirically learns the characteristic patterns associated to a multivariate signals set. Once learned, Multivariate OMP approximates sparsely any signal of this considered set. These methods are specified to the 2D rotation-invariant case. Shift and rotation invariant cases induce a compact learned dictionary. Our methods are applied to 2D handwritten data in order to extract the elementary features of this signals set.
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Dates and versions

hal-00625352 , version 1 (21-09-2011)

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Quentin Barthélemy, Anthony Larue, Aurélien Mayoue, David Mercier, Jerome I. Mars. Multivariate dictionary learning and shift & 2D rotation invariant sparse coding. SSP 2011 - 2011 IEEE Workshop on Statistical Signal Processing, Jun 2011, Nice, France. pp.649-652, ⟨10.1109/SSP.2011.5967783⟩. ⟨hal-00625352⟩
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