The Iteration-Tuned Dictionary for Sparse Representations

Joaquin Zepeda 1, 2 Christine Guillemot 1 Ewa Kijak 2
1 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We introduce a new dictionary structure for sparse representations better adapted to pursuit algorithms used in practical scenarios. The new structure, which we call an Iteration-Tuned Dictionary (ITD), consists of a set of dictionaries each associated to a single iteration index of a pursuit algorithm. In this work we first adapt pursuit decompositions to the case of ITD structures and then introduce a training algorithm used to construct ITDs. The training algorithm consists of applying a K-means to the (i-1)-th residuals of the training set to thus produce the i-th dictionary of the ITD structure. In the results section we compare our algorithm against the state-of-the-art dictionary training scheme and show that our method produces sparse representations yielding better signal approximations for the same sparsity level.
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Conference papers
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https://hal.inria.fr/inria-00539076
Contributor : Joaquin Zepeda <>
Submitted on : Tuesday, November 23, 2010 - 10:41:49 PM
Last modification on : Friday, November 16, 2018 - 1:25:23 AM

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  • HAL Id : inria-00539076, version 1

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Joaquin Zepeda, Christine Guillemot, Ewa Kijak. The Iteration-Tuned Dictionary for Sparse Representations. IEEE International Workshop on Multimedia Signal Processing, Oct 2010, St. Malo, France. ⟨inria-00539076⟩

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