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Improving sparsity of coefficients for robust sparse and collaborative representation-based image classification

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Abstract

Sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) have shown promising classification results. Both methods are distance-based classifiers, and they represent a test sample with coefficients solved by different sparsity regularizations. The reason why the representation coefficient vector can be sparse is that the test sample can be represented by only a small subset of the whole dictionary, which means that only a few entries of the coefficient vector have nonzero values. Previous studies show that sparsity of representation coefficients of SRC and CRC is significant for achieving good classification performance. However, the parameter of the algorithm is closely associated with the sparsity and it is very hard to obtain the optimal parameter value. In this paper, we propose two novel versions of SRC and CRC and implement four algorithms. The first version has two implementation named SQ_SRC and SQ_CRC, which use squared value of each entry of the representation coefficient vector to enhance sparsity of representation coefficients. SQ_SRC and SQ_CRC allow us to obtain more robust representation and classification performance of the test sample. The second version has another two implementation named SQF_SRC and SQF_CRC, which integrate original SRC and CRC with the SQ_SRC and SQ_CRC to perform classification. We conduct extensive experiments on a number of facial datasets, as well as a couple of non-facial datasets. The experimental results demonstrated that our methods output much more promising classification results than naive SRC and CRC in most scenarios.

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References

  1. Cai S, Zhang L, Zuo W, Feng X (2016) A probabilistic collaborative representation based approach for pattern classification. In: IEEE conference on computer vision and pattern recognition (CVPR 2016), p 1

  2. Deng W, Hu J, Guo J (2013) In defense of sparsity based face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 399–406

  3. Georghiades A, et al (1997) Yale face database. Center for Computational Vision and Control at Yale University, p 2. http://cvcyaleedu/projects/yalefaces/yalefa

  4. Huang W, Wang X, Ma Y, Jiang Y, Zhu Y, Jin Z (2015) Robust kernel collaborative representation for face recognition. Opt Eng 54(5):053,103–053,103

    Article  Google Scholar 

  5. Li W, Du Q, Zhang B (2015) Combined sparse and collaborative representation for hyperspectral target detection. Pattern Recognit 48(12):3904–3916

    Article  Google Scholar 

  6. Li Z, Lai Z, Xu Y, Yang J, Zhang D (2015) A locality-constrained and label embedding dictionary learning algorithm for image classification. In: IEEE transactions on neural networks and learning systems, pp 1–16

  7. Liu Z, Song X, Tang Z (2015) Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition. Neural Comput Appl 26(8):2013–2026

    Article  Google Scholar 

  8. Lu Z, Zhang L (2016) Face recognition algorithm based on discriminative dictionary learning and sparse representation. Neurocomputing 174:749–755

    Article  Google Scholar 

  9. Martinez AM (1998) The AR face database. CVC Technical report 24

  10. Mitchell T (1999) CMU face images. https://archive.ics.uci.edu/ml/machine-learning-databases/faces-mld/faces.html. Accessed 9 June 2016

  11. Nene SA, Nayar SK, Murase H, et al (1996) Columbia object image library (coil-20). Technical report CUCS-005-96

  12. Ortiz EG, Becker BC (2014) Face recognition for web-scale datasets. Comput Vis Image Underst 118:153–170

    Article  Google Scholar 

  13. Patel VM, Wu T, Biswas S, Phillips PJ, Chellappa R (2012) Dictionary-based face recognition under variable lighting and pose. IEEE Trans Inf Forensics Secur 7(3):954–965

    Article  Google Scholar 

  14. Peng X, Zhang L, Yi Z, Tan KK (2014) Learning locality-constrained collaborative representation for robust face recognition. Pattern Recognit 47(9):2794–2806

    Article  Google Scholar 

  15. Peng X, Tang H, Zhang L, Yi Z, Xiao S (2016) A unified framework for representation-based subspace clustering of out-of-sample and large-scale data. IEEE Trans Neural Netw Learn Syst 27(12):2499–2512

    Article  MathSciNet  Google Scholar 

  16. Peng X, Yu Z, Yi Z, Tang H (2016) Constructing the L2-graph for robust subspace learning and subspace clustering. IEEE Trans Cybern 99:1–14

    Google Scholar 

  17. Peng Y, Pan Z, Zheng Z, Li X (2016) Hyperspectral image classification by fusion of multiple classifiers. Int J Database Theory Appl 9(2):183–192

    Article  Google Scholar 

  18. Samaria FS, Harter AC (1995) Parameterisation of a stochastic model for human face identification. In: Applications of computer vision. Proceedings of the second IEEE workshop on 1994, pp 138–142

  19. Senthilkumar (2016) Senthil irtt face database version 1.2. https://github.com/zengsn/researches. Accessed 30 Nov 2016

  20. Tang D, Zhu N, Yu F, Chen W, Tang T (2014) A novel sparse representation method based on virtual samples for face recognition. Neural Comput Appl 24(3–4):513–519

    Article  Google Scholar 

  21. Tao J, Hu W, Wen S (2016) Multi-source adaptation joint kernel sparse representation for visual classification. Neural Netw 76:135–151

    Article  Google Scholar 

  22. Weber M (1999) Caltech faces. http://www.vision.caltech.edu/html-files/archive.html. Accessed 7 June 2016

  23. Weber M (1999) Caltech leaves 1999. http://www.vision.caltech.edu/archive.html. Accessed 7 June 2016

  24. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  25. Xu Y, Li Z, Pan JS, Yang JY (2013) Face recognition based on fusion of multi-resolution gabor features. Neural Comput Appl 23(5):1251–1256

    Article  Google Scholar 

  26. Xu Y, Li X, Yang J, Lai Z, Zhang D (2014) Integrating conventional and inverse representation for face recognition. IEEE Trans Cybern 44(10):1738–1746

    Article  Google Scholar 

  27. Xu Y, Zhang B, Zhong Z (2015) Multiple representations and sparse representation for image classification. Pattern Recognit Lett 68:9–14

    Article  Google Scholar 

  28. Xu Y, Fang X, Wu J, Li X, Zhang D (2016) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25(2):850–863

    Article  MathSciNet  Google Scholar 

  29. Xu Y, Zhang Z, Lu G, Yang J (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recognit 54:68–82

    Article  Google Scholar 

  30. Xu Y, Li Z, Zhang B, Yang J, You J (2017) Sample diversity, representation effectiveness and robust dictionary learning for face recognition. Inf Sci 375:171–182

    Article  Google Scholar 

  31. Xu Z, Zhang H, Wang Y, Chang X, Liang Y (2010) L1/2 regularization. Sci China Inf Sci 53(6):1159–1169

    Article  MathSciNet  Google Scholar 

  32. Yang AY, Zhou Z, Balasubramanian AG, Sastry SS, Ma Y (2013) Fast-minimization algorithms for robust face recognition. IEEE Trans Image Process 22(8):3234–3246

    Article  Google Scholar 

  33. Zeng S, Xiong Y (2016) Weighted average integration of sparse representation and collaborative representation for robust face recognition. Comput Vis Media 2(4):357–365

    Article  Google Scholar 

  34. Zeng S, Yang X, Gou J (2016) Multiplication fusion of sparse and collaborative representation for robust face recognition. Multimed Tools Appl. doi:10.1007/s11042-016-4035-5

    Article  Google Scholar 

  35. Zeng S, Yang X, Gou J, Wen J (2016) Integrating absolute distances in collaborative representation for robust image classification. CAAI Trans Intell Technol 1(2):189–196

    Article  Google Scholar 

  36. Zhang B, Mu Z, Li C, Zeng H (2014) Robust classification for occluded ear via gabor scale feature-based non-negative sparse representation. Opt Eng 53(6):061,702–061,702

    Article  Google Scholar 

  37. Zhang B, Ji S, Li L, Zhang S, Yang W (2016) Sparsity analysis versus sparse representation classifier. Neurocomputing 171:387–393

    Article  Google Scholar 

  38. Zhang H, Wang F, Chen Y, Zhang W, Wang K, Liu J (2016) Sample pair based sparse representation classification for face recognition. Expert Syst Appl 45:352–358

    Article  Google Scholar 

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

  40. Zhang L, Yang M, Feng X, Ma Y, Zhang D (2012) Collaborative representation based classification for face recognition. arXiv preprint arXiv:12042358 1

  41. Zhang Q, Cai Y, Xu X (2013a) Maximum margin sparse representation discriminative mapping with application to face recognition. Opt Eng 52(2):027,202–027,202

    Article  Google Scholar 

  42. Zhang Q, Fu Y, Li H, Zou J (2013b) Dictionary learning method for joint sparse representation-based image fusion. Opt Eng 52(5):057,006–057,006

    Article  Google Scholar 

  43. Zhang Z, Li Z, Xie B, Wang L, Chen Y (2014) Integrating globality and locality for robust representation based classification. Math Probl Eng 2014(1):12–25

    Google Scholar 

  44. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530

    Article  Google Scholar 

  45. Zhu Q, Xu Y (2013) Multi-directional two-dimensional pca with matching score level fusion for face recognition. Neural Comput Appl 23(1):169–174

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by Natural Science Foundation of China (Grant No. 61502208), China Postdoctoral Science Foundation (Grant No. 2015M570 411), Natural Science Foundation of Jiangsu Province of China (Grant No. BK20150522), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 14KJB520007), Innovation Committee of Science and Technology of Shenzhen (Grant No. JCYJ20130329154017293) and Science and Technology Program of Huizhou (Grant No. 2015 B010002005).

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Correspondence to Shaoning Zeng.

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Zeng, S., Gou, J. & Yang, X. Improving sparsity of coefficients for robust sparse and collaborative representation-based image classification. Neural Comput & Applic 30, 2965–2978 (2018). https://doi.org/10.1007/s00521-017-2900-4

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