DocumentCode :
70215
Title :
Scale Adaptive Dictionary Learning
Author :
Cewu Lu ; Jianping Shi ; Jiaya Jia
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Volume :
23
Issue :
2
fYear :
2014
fDate :
Feb. 2014
Firstpage :
837
Lastpage :
847
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.
Keywords :
image reconstruction; image texture; learning (artificial intelligence); image processing tasks; scale adaptive dictionary learning; sparse reconstruction; texture data sets; training data; video data sets; Adaptation models; Computational modeling; Dictionaries; Image processing; Optimization; Vectors; Visualization; Dictionary learning; image restoration; sparse coding; sparse representation;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2013.2287602
Filename :
6648711
Link To Document :
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