NDLI: Multi-attributed Dictionary Learning for Sparse Coding

Content Provider	IEEE Xplore Digital Library
Author	Chen-Kuo Chiang Te-Feng Su Chih Yen Shang-Hong Lai
Copyright Year	2013
Description	Author affiliation: Nat. Tsing Hua Univ., Hsinchu, Taiwan (Chen-Kuo Chiang; Te-Feng Su; Chih Yen; Shang-Hong Lai)
Abstract	We present a multi-attributed dictionary learning algorithm for sparse coding. Considering training samples with multiple attributes, a new distance matrix is proposed by jointly incorporating data and attribute similarities. Then, an objective function is presented to learn category-dependent dictionaries that are compact (closeness of dictionary atoms based on data distance and attribute similarity), reconstructive (low reconstruction error with correct dictionary) and label-consistent (encouraging the labels of dictionary atoms to be similar). We have demonstrated our algorithm on action classification and face recognition tasks on several publicly available datasets. Experimental results with improved performance over previous dictionary learning methods are shown to validate the effectiveness of the proposed algorithm.
Sponsorship	IEEE Comput. Soc.
Starting Page	1137
Ending Page	1144
File Size	962922
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Training Linear programming Image reconstruction Face recognition Image coding Lighting sparse coding Dictionary learning multiple attributes
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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