Full Text:   <1037>

Summary:  <150>

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: 2223

Citations:  Bibtex RefMan EndNote GB/T7714


San-yuan Zhang

http://orcid.org/0000-0001-8604- 874X

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.11 P.1795-1805


Laplacian sparse dictionary learning for image classification based on sparse representation

Author(s):  Fang Li, Jia Sheng, San-yuan Zhang

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   syzhang@zju.edu.cn

Key Words:  Sparse representation, Laplacian regularizer, Dictionary learning, Double sparsity, Manifold

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",
publisher="Zhejiang University Press & Springer",

%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
%V 18
%N 11
%P 1795-1805
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1600039

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
ER -
DOI - 10.1631/FITEE.1600039

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.


概要:稀疏表示作为数据表示的一种数学模型,是解决模式识别、机器学习、计算机视觉等领域问题的有力工具。字典学习是稀疏表示方法的重要组成部分,在对原始信号及其在字典学习空间中的重建误差的最小化上发挥着重要的作用。在稀疏表示模型中,直接利用训练样本作为字典可以取得良好的性能。但由于训练样本含有噪声,这样的字典很大且效率低下。为取得更小且表现更好的字典,本文提出一种基于流形学习及双稀疏理论的拉普拉斯稀疏字典学习方法(Laplacian sparse dictionary, LSD)。本文将拉普拉斯权重图加入稀疏表示的模型,并对字典加以l1范数约束。LSD是一个稀疏的过完备字典,可保持数据的内在结构,并为每个类学习一个更小的字典。学习得到的字典可以嵌入基于稀疏表示的分类框架。将本文提出的方法和其它方法在三个基准的约束人脸数据(Extended Yale B、ORL、AR)和一个无约束的行人数据图像数据库i-LIDS-MA上进行对比实验。结果显示本文提出的LSD算法比当前基于分类的稀疏表示的方法更有优势。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Aharon, M., Elad, M., Bruckstein, A., 2006. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process., 54(11): 4311-4322.

[2]Bąk, S., Corvee, E., Bremond, F., et al., 2012. Boosted human re-identification using Riemannian manifolds. Image Vis. Comput., 30(6):443-452.

[3]Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J., 1997. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Intell., 19(7):711-720.

[4]Belkin, M., Niyogi, P., 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, p.585-591.

[5]Chapelle, O., Schölkopf, B., Zien, A., 2006. Semi-supervised Learning. MIT Press, Cambridge, MA.

[6]Elhamifar, E., Vidal, R., 2013. Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans. Patt. Anal. Mach. Intell., 35(11):2765-2781.

[7]Gangeh, M.J., Ghodsi, A., Kamel, M.S., 2013. Kernelized supervised dictionary learning. IEEE Trans. Signal Process., 61(19):4753-4767.

[8]Gao, S., Tsang, I.W.H., Ma, Y., 2014. Learning category-specific dictionary and shared dictionary for fine-grained image categorization. IEEE Trans. Image Process., 23(2): 623-634.

[9]Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J., 2001. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Patt. Anal. Mach. Intell., 23(6):643-660.

[10]He, X., Niyogi, P., 2003. Locality preserving projections. 17th Annual Conf. on Neural Information Processing Systems, p.186-197.

[11]He, X., Yan, S., Hu, Y., et al., 2005. Face recognition using Laplacian faces. IEEE Trans. Patt. Anal. Mach. Intell., 27(3):328-340.

[12]Huang, M., Yang, W., Jiang, J., et al., 2014. Brain extraction based on locally linear representation-based classification. NeuroImage, 92:322-339.

[13]Lee, H., Battle, A., Raina, R., et al., 2006. Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, p.801-808.

[14]Lee, K.C., Ho, J., Kriegman, D.J., 2005. Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Patt. Anal. Mach. Intell., 27(5):684-698.

[15]Lu, X., Li, X., 2014. Group sparse reconstruction for image segmentation. Neurocomputing, 136:41-48.

[16]Lu, X., Wu, H., Yuan, Y., et al., 2013. Manifold regularized sparse NMF for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens., 51(5):2815-2826.

[17]Lu, Y., Lai, Z., Fan, Z., et al., 2015. Manifold discriminant regression learning for image classification. Neurocomputing, 166:475-486.

[18]Martinez, A.M., Benavente, R., 1998. The AR Face Database. CVC Technical Report, No. 24. Centre de Visió per Computador, Universitat Autònoma de Barcelona, Edifici O, Bellaterra, Barcelona.

[19]Peleg, T., Elad, M., 2014. A statistical prediction model based on sparse representations for single image super- resolution. IEEE Trans. Image Process., 23(6):2569-2582.

[20]Qiao, L., Chen, S., Tan, X., 2010. Sparsity preserving projections with applications to face recognition. Patt. Recogn., 43(1):331-341.

[21]Roweis, S.T., Saul, L.K., 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500): 2323-2326.

[22]Rubinstein, R., Bruckstein, A.M., Elad, M., 2010a. Dictionaries for sparse representation modeling. Proc. IEEE, 98(6):1045-1057.

[23]Rubinstein, R., Zibulevsky, M., Elad, M., 2010b. Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans. Signal Process., 58(3): 1553-1564.

[24]Shao, L., Yan, R., Li, X., et al., 2014. From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms. IEEE Trans. Cybern., 44(7):1001-1013.

[25]Tenenbaum, J.B., de Silva, V., Langford, J.C., 2000. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323.

[26]Tibshirani, R., 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.), 58(2):267-288.

[27]Turk, M., Pentland, A., 1991. Eigenfaces for recognition. J. Cogn. Neurosci., 3(1):71-86.

[28]Wang, W., Wang, R., Huang, Z., et al., 2015. Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.2048-2057.

[29]Wright, J., Yang, A.Y., Ganesh, A., et al., 2009. Robust face recognition via sparse representation. IEEE Trans. Patt. Anal. Mach. Intell., 31(2):210-227.

[30]Yang, A.Y., Zhou, Z., Balasubramanian, A.G., et al., 2013. Fast-l1 minimization algorithms for robust face recognition. IEEE Trans. Image Process., 22(8):3234-3246.

[31]Yang, J., Zhang, L., Xu, Y., et al., 2012. Beyond sparsity: the role of l1-optimizer in pattern classification. Patt. Recogn., 45(3):1104-1118.

[32]Yang, J.F., Zhang, Y., 2011. Alternating direction algorithms for -problems in compressive sensing. SIAM J. Sci. Comput., 33(1):250-278.

[33]Yang, M., Zhang, L., Yang, J., et al., 2010. Metaface learning for sparse representation-based face recognition. 17th IEEE Int. Conf. on Image Processing, p.1601-1604.

[34]Yang, M., van Gool, L., Zhang, L., 2013. Sparse variation dictionary learning for face recognition with a single training sample per person. IEEE Int. Conf. on Computer Vision, p.689-696.

[35]Yang, M., Dai, D., Shen, L., et al., 2014. Latent dictionary learning for sparse representation-based classification. IEEE Conf. on Computer Vision and Pattern Recognition, p.4138-4145.

[36]Zhang, Z., Xu, Y., Yang, J., et al., 2015. A survey of sparse representation: algorithms and applications. IEEE Access, 3:490-530.

[37]Zheng, M., Bu, J., Chen, C., et al., 2011. Graph regularized sparse coding for image representation. IEEE Trans. Image Process., 20(5):1327-1336.

[38]Zhu, P., Zuo, W., Zhang, L., et al., 2014. Image set-based collaborative representation for face recognition. IEEE Trans. Inform. Forens. Secur., 9(7):1120-1132.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE