NDLI: Submodular dictionary learning for sparse coding

Content Provider	IEEE Xplore Digital Library
Author	Zhuolin Jiang Guangxiao Zhang Davis, L.S.
Copyright Year	2012
Description	Author affiliation: Institute for Advanced Computer Studies, University of Maryland, College Park, MD, 20742 (Zhuolin Jiang; Guangxiao Zhang; Davis, L.S.)
Abstract	A greedy-based approach to learn a compact and discriminative dictionary for sparse representation is presented. We propose an objective function consisting of two components: entropy rate of a random walk on a graph and a discriminative term. Dictionary learning is achieved by finding a graph topology which maximizes the objective function. By exploiting the monotonicity and submodularity properties of the objective function and the matroid constraint, we present a highly efficient greedy-based optimization algorithm. It is more than an order of magnitude faster than several recently proposed dictionary learning approaches. Moreover, the greedy algorithm gives a near-optimal solution with a (1/2)-approximation bound. Our approach yields dictionaries having the property that feature points from the same class have very similar sparse codes. Experimental results demonstrate that our approach outperforms several recently proposed dictionary learning techniques for face, action and object category recognition.
Starting Page	3418
Ending Page	3425
File Size	374323
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6248082
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Entropy Encoding Image color analysis Training Matching pursuit algorithms Partitioning algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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