TY  - CHAP
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6738031&isnumber=6737993
KW  - Dictionary learning; K-SVD; NMF; sparse representations

Y1  - 2013/09//
TI  - K-WEB: Nonnegative dictionary learning for sparse image representations
AV  - none
SP  - 146
N2  - 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 ?0-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.
PB  - IEEE
A1  - Bevilacqua, Marco
A1  - Roumy, Aline
A1  - Guillemot, Christine
A1  - Alberi-Morel, Marie Line
EP  - 150
T2  - Proceedings of the 20th IEEE International Conference on Image Processing (ICIP)
ID  - eprints2415
N1  - 20th IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, 15-18 September 2013 
ER  -