Abstract
In recent years, dictionary learning has been widely used in various image classification applications. However, how to construct an effective dictionary for robust image classification task, in which both the training and the testing image samples are corrupted, is still an open problem. To address this, we propose a novel low-rank double dictionary learning (LRD2L) method. Unlike traditional dictionary learning methods, LRD2L simultaneously learns three components from training data: (1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative features owned by each class, (2) a low-rank class-shared dictionary which models the common patterns shared by different classes and (3) a sparse error container to fit the noises in data. As a result, the class-specific information, the class-shared information and the noises contained in data are separated from each other. Therefore, the dictionaries learned by LRD2L are noiseless, and the class-specific sub-dictionary of each class can be more discriminative. Also since the common features across different classes, which are essential to the reconstruction of image samples, are preserved in class-shared dictionary, LRD2L has a powerful reconstructive capability for newly coming testing samples. Experimental results on three public available datasets reveal the effectiveness and the superiority of our approach compared to the state-of-the-art dictionary learning methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, M., Van Gool, L., Kong, H.: Sparse variation dictionary learning for face recognition with a single training sample per person. In: ICCV (2013)
Yang, M., Zhang, L., Yang, J., Zhang, D.: Metaface learning for sparse representation based face recognition. In: ICIP (2010)
Li, S., Yin, H., Fang, L., Member, S.: Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Trans. Biomed. Eng. 59(12), 3450–3459 (2012)
Rubinstein, R., Bruckstein, A.M., Elad, M.: Dictionaries for sparse representation modeling. Proc. IEEE 98(6), 1045–1057 (2010)
Wright, B.J., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Zhang, Q., Li, B.: Discriminative K-SVD for dictionary learning in face recognition. In: CVPR (2010)
Jiang, Z., Lin, Z., Davis, L.S.: Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2651–2664 (2013)
Ramirez, I., Sprechmann, P., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: CVPR (2010)
Yang, M., Zhang, D., Feng, X.: Fisher discrimination dictionary learning for sparse representation. In: ICCV (2011)
Kong, S., Wang, D.: A dictionary learning approach for classification: separating the particularity and the commonality. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 186–199. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33718-5_14
Gu, S., Zhang, L., Zuo, W., Feng, X.: Projective dictionary pair learning for pattern classification. In: NIPS (2014)
Ma, L., Wang, C., Xiao, B., Zhou, W.: Sparse representation for face recognition based on discriminative low-rank dictionary learning. In: CVPR (2012)
Li, L., Li, S., Fu, Y.: Learning low-rank and discriminative dictionary for image classification. Image Vis. Comput. 32(10), 814–823 (2014)
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: NIPS (2011)
Zhuang, L., Gao, S., Tang, J., Wang, J.: Constructing a nonnegative low-rank and sparse graph with data-adaptive features. IEEE Trans. Image Process. 24(11), 3717–3728 (2015)
Li, S., Fu, Y.: Low-rank coding with b-matching constraint for semi-supervised classification. In: IJCAI (2013)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)
Martinez, A.M.: The AR face database. CVC Technical report (1998)
Chen, C.F., Wei, C.P., Wang, Y.C.F.: Low-rank matrix recovery with structural incoherence for robust face recognition. In: CVPR (2012)
Zhang, Y., Jiang, Z., Davis, L.S., Park, C.: Learning structured low-rank representations for image classification. In: CVPR (2013)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-20). Technical report No. CUCS-006-96 (1996)
Wang, S., Fu, Y.: Locality-constrained discriminative learning and coding. In: CVPR Workshops (2015)
Li, S., Fu, Y.: Learning robust and discriminative subspace with low-rank constraints. IEEE Trans. Neural Netw. Learn. Syst. 27, 2160–2173(2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Rong, Y., Xiong, S., Gao, Y. (2017). Robust Image Classification via Low-Rank Double Dictionary Learning. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-51811-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51810-7
Online ISBN: 978-3-319-51811-4
eBook Packages: Computer ScienceComputer Science (R0)