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K-WEB: Nonnegative dictionary learning for sparse image representations

Abstract : This paper presents a new nonnegative dictionary learning method, to decompose an input data matrix into a dictionary of nonnegative atoms, and a representation matrix with a strict l0-sparsity constraint. This constraint makes each input vector representable by a limited combination of atoms. The proposed method consists of two steps which are alternatively iterated: a sparse coding and a dictionary update stage. As for the dictionary update, an original method is proposed, which we call K-WEB, as it involves the computation of k WEighted Barycenters. The so designed algorithm is shown to outperform other methods in the literature that address the same learning problem, in different applications, and both with synthetic and "real" data, i.e. coming from natural images.
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Submitted on : Wednesday, October 23, 2013 - 2:39:36 PM
Last modification on : Tuesday, October 19, 2021 - 11:58:56 PM
Long-term archiving on: : Friday, January 24, 2014 - 4:25:48 AM


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  • HAL Id : hal-00876018, version 1


Marco Bevilacqua, Aline Roumy, Christine Guillemot, Marie-Line Alberi Morel. K-WEB: Nonnegative dictionary learning for sparse image representations. IEEE International Conference on Image Processing (ICIP), Sep 2013, Melbourne, Australia. ⟨hal-00876018⟩



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