Abstract
Sparse representation of images by analysis dictionary learning (ADL) has been an active topic in pattern classification as samples can be transformed into sparse representation efficiently. However, learning a discriminative and compact analysis dictionary (ADL) has not been well addressed when the samples are corrupted with noises. In this paper, we propose a low-rank orthonormal analysis dictionary learning (LR-OADL) model. Specially, the low-rank constraint is firstly imposed on the analysis representation to handle the possible noises in the samples. With orthonormal constraint and off-block diagonal supressing term, the analysis dictionary atoms from different classes are incoherent from each other, leading to discriminative block-diagonal representations. Furthermore, a novel locality constraint is exploited to promote the discriminative within class representation similarity. Finally, we employ an alternating minimization algorithm to solve this problem. Experiments on benchmark image datasets demonstrate the efficacy of the proposed LR-OADL model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311–4322 (2006). https://doi.org/10.1109/TSP.2006.881199
Chen, C.F., Wei, C.P., Wang, Y.C.F.: Low-rank matrix recovery with structural incoherence for robust face recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2618–2625 (2012). https://doi.org/10.1109/CVPR.2012.6247981
Dong, Z., Pei, M., Jia, Y.: Discriminative orthonormal dictionary learning for fast low-rank representation. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) Neural Information Processing, pp. 79–89. Springer International Publishing, Cham (2015)
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). https://doi.org/10.1109/TPAMI.2013.88
Li, Z., Zhang, Z., Qin, J., Li, S., Cai, H.: Low-rank analysis-csynthesis dictionary learning with adaptively ordinal locality. Neural Netw. 119, 93–112 (2019). https://doi.org/10.1016/j.neunet.2019.07.013, https://www.sciencedirect.com/science/article/pii/S0893608019302011
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. ArXiv http://arxiv.org/abs/1009.5055 (2009)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013). https://doi.org/10.1109/TPAMI.2012.88
Ravishankar, S., Bresler, Y.: Learning sparsifying transforms. IEEE Trans. Sig. Process. 61(5), 1072–1086 (2013). https://doi.org/10.1109/TSP.2012.2226449
Rubinstein, R., Peleg, T., Elad, M.: Analysis K-SVD: a dictionary-learning algorithm for the analysis sparse model. IEEE Trans. Sig. Process. 61(3), 661–677 (2013). https://doi.org/10.1109/TSP.2012.2226445
Sadanand, S., Corso, J.J.: Action bank: a high-level representation of activity in video. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1234–1241 (2012). https://doi.org/10.1109/CVPR.2012.6247806
Shekhar, S., Patel, V.M., Chellappa, R.: Analysis sparse coding models for image-based classification. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5207–5211 (2014). https://doi.org/10.1109/ICIP.2014.7026054
Wang, J., Guo, Y., Guo, J., Li, M., Kong, X.: Synthesis linear classifier based analysis dictionary learning for pattern classification. Neurocomputing 238, 103–113 (2017). https://doi.org/10.1016/j.neucom.2017.01.041, https://www.sciencedirect.com/science/article/pii/S0925231217301157
Wang, J., Guo, Y., Guo, J., Luo, X., Kong, X.: Class-aware analysis dictionary learning for pattern classification. IEEE Sig. Process. Lett. 24(12), 1822–1826 (2017). https://doi.org/10.1109/LSP.2017.2734860
Wang, Q., Guo, Y., Guo, J., Kong, X.: Synthesis K-SVD based analysis dictionary learning for pattern classification. Multimedia Tools Appl. 77(13), 17023–17041 (2018). https://doi.org/10.1007/s11042-017-5269-6
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009). https://doi.org/10.1109/TPAMI.2008.79
Yi, Y., Wang, J., Zhou, W., Zheng, C., Kong, J., Qiao, S.: Non-negative matrix factorization with locality constrained adaptive graph. IEEE Trans. Circuits Syst. Video Technol. 30(2), 427–441 (2020). https://doi.org/10.1109/TCSVT.2019.2892971
Yin, H., Wu, X., Kittler, J.: Face recognition via locality constrained low rank representation and dictionary learning. CoRR abs/1912.03145 http://arxiv.org/abs/1912.03145 (2019)
Yin, H., Wu, X., Kittler, J., Feng, Z.: Learning a representation with the block-diagonal structure for pattern classification. Pattern Anal. Appl. 23(3), 1381–1390 (2020). https://doi.org/10.1007/s10044-019-00858-4
Zhang, Q., Li, B.: Discriminative K-SVD for dictionary learning in face recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2691–2698 (2010). https://doi.org/10.1109/CVPR.2010.5539989
Zhang, Z., Xu, Y., Shao, L., Yang, J.: Discriminative block-diagonal representation learning for image recognition. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 3111–3125 (2018). https://doi.org/10.1109/TNNLS.2017.2712801
Zheng, M., et al.: Graph regularized sparse coding for image representation. IEEE Trans. Image Process. 20(5), 1327–1336 (2011). https://doi.org/10.1109/TIP.2010.2090535
Acknowledgements
This work is supported by the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2021JM-339).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, K., Liu, Z., Sun, Q. (2021). Low-Rank Orthonormal Analysis Dictionary Learning for Image Classification. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13033. Springer, Cham. https://doi.org/10.1007/978-3-030-89370-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-89370-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89369-9
Online ISBN: 978-3-030-89370-5
eBook Packages: Computer ScienceComputer Science (R0)