Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/82692
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Type: Conference paper
Title: Fast convolutional sparse coding
Author: Bristow, H.
Eriksson, A.
Lucey, S.
Citation: Proceedings, 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, 23-28 June 2013, Portland, Oregon, USA: pp. 391-398
Publisher: IEEE
Publisher Place: United States of America
Issue Date: 2013
ISBN: 9780769549897
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (26th : 2013 : Portland, Oregon)
Statement of
Responsibility: 
Hilton Bristow, Anders Eriksson and Simon Lucey
Abstract: Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably more complex than traditional sparse coding. In this paper, we draw upon ideas from signal processing and Augmented Lagrange Methods (ALMs) to produce a fast algorithm with globally optimal sub problems and super-linear convergence.
Rights: © 2013 IEEE
DOI: 10.1109/CVPR.2013.57
Description (link): http://www.pamitc.org/cvpr13/
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Computer Science publications

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