NDLI: Scale Adaptive Dictionary Learning

Content Provider	IEEE Xplore Digital Library
Author	Cewu Lu Jianping Shi Jiaya Jia
Copyright Year	1992
Abstract	Dictionary learning has been widely used in many image processing tasks. In most of these methods, the number of basis vectors is either set by experience or coarsely evaluated empirically. In this paper, we propose a new scale adaptive dictionary learning framework, which jointly estimates suitable scales and corresponding atoms in an adaptive fashion according to the training data, without the need of prior information. We design an atom counting function and develop a reliable numerical scheme to solve the challenging optimization problem. Extensive experiments on texture and video data sets demonstrate quantitatively and visually that our method can estimate the scale, without damaging the sparse reconstruction ability.
Sponsorship	IEEE Signal Processing Society
Page Count	11
File Size	3702805
Starting Page	837
Ending Page	847
File Format	PDF
ISSN	10577149
Volume Number	23
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Vectors Optimization Image processing Computational modeling Visualization Adaptation models image restoration Dictionary learning sparse coding sparse representation
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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