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Sparse image coding using learned overcomplete dictionaries

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2 Author(s)
Murray, J.F. ; Electr. & Comput. Eng., California Univ., San Diego, CA ; Kreutz-Delgado, K.

Images can be coded accurately using a sparse set of vectors from an overcomplete dictionary, with potential applications in image compression and feature selection for pattern recognition. We discuss algorithms that perform sparse coding and make three contributions. First, we compare our overcomplete dictionary learning algorithm (FOCUSS-CNDL) with overcomplete independent component analysis (ICA). Second, noting that once a dictionary has been learned in a given domain the problem becomes one of choosing the vectors to form an accurate, sparse representation, we compare a recently developed algorithm (sparse Bayesian learning with adjustable variance Gaussians) to well known methods of subset selection: matching pursuit and FOCUSS. Third, noting that in some cases it may be necessary to find a non-negative sparse coding, we present a modified version of the FOCUSS algorithm that can find such non-negative codings

Published in:

Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop

Date of Conference:

Sept. 29 2004-Oct. 1 2004