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K-WEB: Nonnegative dictionary learning for sparse image representations

Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line K-WEB: Nonnegative dictionary learning for sparse image representations. In: Proceedings of the 20th IEEE International Conference on Image Processing (ICIP). IEEE, pp. 146-150. (2013)

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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.

Item Type: Book Section
Identification Number: 10.1109/ICIP.2013.6738031
Additional Information: 20th IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, 15-18 September 2013
Uncontrolled Keywords: Dictionary learning; K-SVD; NMF; sparse representations
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Research Area: Computer Science and Applications
Depositing User: Ms T. Iannizzi
Date Deposited: 11 Dec 2014 11:25
Last Modified: 11 Dec 2014 11:34
URI: http://eprints.imtlucca.it/id/eprint/2415

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