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Learning a representation with the block-diagonal structure for pattern classification

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Abstract

Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns representation with block-diagonal structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method. The source code of our proposed RBDS is accessible at https://github.com/yinhefeng/RBDS.

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References

  1. Zheng J, Qiu H, Sheng W, Yang X, Yu H (2018) Kernel group sparse representation classifier via structural and non-convex constraints. Neurocomputing 296:1–11

    Article  Google Scholar 

  2. Shao C, Song X, Feng ZH et al (2017) Dynamic dictionary optimization for sparse-representation-based face classification using local difference images. Inf Sci 393:1–14

    Article  Google Scholar 

  3. Liu G (2018) Robust visual tracking via smooth manifold kernel sparse learning. IEEE Trans Multimed 20(11):2949–2963

    Article  Google Scholar 

  4. Wang J, Shi D, Cheng D, Zhang Y, Gao J (2016) LRSR: low-rank-sparse representation for subspace clustering. Neurocomputing 214:1026–1037

    Article  Google Scholar 

  5. Song X, Feng Z, Hu G et al (2018) Dictionary integration using 3D morphable face models for pose-invariant collaborative-representation-based classification. IEEE Trans Inf Forensics Secur 13(11):2734–2745

    Article  Google Scholar 

  6. Chhatrala R, Patil S, Lahudkar S, Jadhav D (2019) Sparse multilinear Laplacian discriminant analysis for gait recognition. Pattern Anal Appl 22(2):505–518

    Article  MathSciNet  Google Scholar 

  7. Song X, Feng Z, Hu G et al (2017) Half-face dictionary integration for representation-based classification. IEEE Trans Cybern 47(1):142–152

    Article  Google Scholar 

  8. Wright J, Yang A, Ganesh A et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  9. Candès E, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58(3):11

    Article  MathSciNet  Google Scholar 

  10. Liu G, Lin Z, Yan S et al (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184

    Article  Google Scholar 

  11. Ma L, Wang C, Xiao B, Zhou W (2012) Sparse representation for face recognition based on discriminative low-rank dictionary learning. In: 2012 IEEE conference on computer vision and pattern recognition, pp 2586–2593

  12. Zhang Y, Jiang Z, Davis L (2013) Learning structured low-rank representations for image classification. In: 2013 IEEE conference on computer vision and pattern recognition, pp 676–683

  13. Li L, Li S, Fu Y (2014) Learning low-rank and discriminative dictionary for image classification. Image Vis Comput 32(10):814–823

    Article  Google Scholar 

  14. Nguyen H, Yang W, Sheng B, Sun C (2016) Discriminative low-rank dictionary learning for face recognition. Neurocomputing 173:541–551

    Article  Google Scholar 

  15. Zheng Z, Yu M, Jia J et al (2014) Fisher discrimination based low rank matrix recovery for face recognition. Pattern Recogn 47(11):3502–3511

    Article  Google Scholar 

  16. Wei C, Chen C, Wang Y (2014) Robust face recognition with structurally incoherent low-rank matrix decomposition. IEEE Trans Image Process 23(8):3294–3307

    Article  MathSciNet  Google Scholar 

  17. Yin H, Wu X (2016) Face recognition based on structural incoherence and low rank projection. In: 2016 International conference on intelligent data engineering and automated learning, pp 68–78

  18. Chen J, Zhang Y (2014) Sparse representation for face recognition by discriminative low-rank matrix recovery. J Vis Commun Image Represent 25(5):763–773

    Article  Google Scholar 

  19. Dong Z, Pei M, Jia Y (2016) Orthonormal dictionary learning and its application to face recognition. Image Vis Comput 51:13–21

    Article  Google Scholar 

  20. Rong Y, Xiong S, Gao Y (2017) Low-rank double dictionary learning from corrupted data for robust image classification. Pattern Recogn 72:419–432

    Article  Google Scholar 

  21. Gao G, Yang J, Jing XY et al (2017) Learning robust and discriminative low-rank representations for face recognition with occlusion. Pattern Recogn 66:129–143

    Article  Google Scholar 

  22. Du H, Zhao Z, Wang S, Zhang F (2018) Discriminative low-rank graph preserving dictionary learning with Schatten-p quasi-norm regularization for image recognition. Neurocomputing 275:697–710

    Article  Google Scholar 

  23. Wu C, Ding J (2018) Occluded face recognition using low-rank regression with generalized gradient direction. Pattern Recogn 80:256–268

    Article  Google Scholar 

  24. Li Y, Liu J, Lu H, Ma S (2014) Learning robust face representation with classwise block-diagonal structure. IEEE Trans Inf Forensics Secur 9(12):2051–2062

    Article  Google Scholar 

  25. Zhang Z, Xu Y, Shao L, Yang J (2018) Discriminative block-diagonal representation learning for image recognition. IEEE Trans Neural Netw Learn Syst 29(7):3111–3125

    Article  MathSciNet  Google Scholar 

  26. Lin Z, Liu R, Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. In: 2011 advances in neural information processing systems, pp 612–620

  27. Georghiades A, Belhumeur P, Kriegman D (2001) 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

    Article  Google Scholar 

  28. Martinez AM (1998) The AR face database. CVC technical report, p 24

  29. Samaria F, Harter A (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the second IEEE workshop on applications of computer vision, pp 138–142

  30. Huang G, Mattar M, Berg T et al (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49. University of Massachusetts, Amherst

  31. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE conference on computer vision and pattern recognition, pp 2169–2178

  32. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: 2011 IEEE international conference on computer vision, pp 543–550

  33. Wang J, Yang J, Yu K, Lv F, Huang T (2010) Locality-constrained linear coding for image classification. In: 2010 IEEE conference on computer vision and pattern recognition, pp 3360–3367

  34. Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: 2011 IEEE international conference on computer vision, pp 471–478

  35. Jiang Z, Lin Z, Davis L (2013) Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651–2664

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation of China (61672265, U1836218, 61902153, 61876072), the 111 Project of the Ministry of Education of China (B12018), the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant No. KYLX_1123, the Overseas Studies Program for Postgraduates of Jiangnan University and the China Scholarship Council (CSC, No.201706790096), the EPSRC programme grant (FACER2VM) under the number EP/N007743/1, the U.S. Army Research Laboratory, the U. S. Army Research Office, the U.K. Ministry of Defence and the U.K. EPSRC grant under the number EP/R013616/1.

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Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    He-Feng Yin & Xiao-Jun Wu

  2. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK

    Josef Kittler & Zhen-Hua Feng

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  1. He-Feng Yin
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  2. Xiao-Jun Wu
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  3. Josef Kittler
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  4. Zhen-Hua Feng
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Correspondence to Xiao-Jun Wu.

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Yin, HF., Wu, XJ., Kittler, J. et al. Learning a representation with the block-diagonal structure for pattern classification. Pattern Anal Applic 23, 1381–1390 (2020). https://doi.org/10.1007/s10044-019-00858-4

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