CLC number: TP39
On-line Access: 2018-01-12
Received: 2016-01-25
Revision Accepted: 2016-05-12
Crosschecked: 2017-11-20
Cited: 0
Clicked: 7570
Fang Li, Jia Sheng, San-yuan Zhang. Laplacian sparse dictionary learning for image classification based on sparse representation[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1795-1805.
@article{title="Laplacian sparse dictionary learning for image classification based on sparse representation",
author="Fang Li, Jia Sheng, San-yuan Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="11",
pages="1795-1805",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1600039"
}
%0 Journal Article
%T Laplacian sparse dictionary learning for image classification based on sparse representation
%A Fang Li
%A Jia Sheng
%A San-yuan Zhang
%J Frontiers of Information Technology & Electronic Engineering
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%P 1795-1805
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1600039
TY - JOUR
T1 - Laplacian sparse dictionary learning for image classification based on sparse representation
A1 - Fang Li
A1 - Jia Sheng
A1 - San-yuan Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 11
SP - 1795
EP - 1805
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1600039
Abstract: sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As one of the building blocks of the sparse representation method, dictionary learning plays an important role in the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although using training samples directly as dictionary bases can achieve good performance, the main drawback of this method is that it may result in a very large and inefficient dictionary due to noisy training instances. To obtain a smaller and more representative dictionary, in this paper, we propose an approach called Laplacian sparse dictionary (LSD) learning. Our method is based on manifold learning and double sparsity. We incorporate the Laplacian weighted graph in the sparse representation model and impose the l1-norm sparsity on the dictionary. An LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data and learn a smaller dictionary for each class. The learned LSD can be easily integrated into a classification framework based on sparse representation. We compare the proposed method with other methods using three benchmark-controlled face image databases, Extended Yale B, ORL, and AR, and one uncontrolled person image dataset, i-LIDS-MA. Results show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation based classification methods.
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