Skip to main content
SpringerLink
Log in

Improved image representation and sparse representation for image classification

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

It seems that for multiple available images of the same object, the pixel values at the same image position are almost always different, which is especially obvious for the deformable object. This implies that it will be not easy to correctly classify the deformable object. In order to extract salient features of images and improve the performance of image classification, a novel image classification algorithm is proposed in this paper. The algorithm can effectively preserve the large-scale information and global features of the original image, reduce the difference in different images of the same object, and significantly improve the accuracy of image classification. Firstly, the virtual image is generated by the new image representation procedure. Secondly, the image classification algorithm is used to obtain the corresponding classification scores of the original image and the virtual image, respectively. Finally, the ultimate classification score is obtained by a simple and efficient score fusion scheme. A large number of experiments on three widely used image databases show that the proposed algorithm outperforms other state-of-the-art algorithms in classification accuracy. At the same time, the algorithm has the advantages of simple implementation and high computational efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Pishchulin L, Gass T, Dreuw P, Ney H (2012) Image warping for face recognition: from local optimality towards global optimization. Pattern Recogn 45(9):3131–3140

    Article  Google Scholar 

  2. Kautkar SN, Atkinson GA, Smith ML (2012) Face recognition in 2D and 2.5D using ridgelets and photometric stereo. Pattern Recogn 45(9):3317–3327

    Article  Google Scholar 

  3. Wang J, You J, Li Q, Xu Y (2012) Orthogonal discriminant vector for face recognition across pose. Pattern Recogn 45(12):4069–4079

    Article  Google Scholar 

  4. Patil HY, Kothari A, Bhurchandi KM (2015) Expression invariant face recognition using semidecimated DWT, Patch-LDSMT, feature and score level fusion.[J]. Appl Intell 127(5):1–18

    Google Scholar 

  5. Jian M, Lam KM, Dong J (2011) Illumination compensation and enhancement for face recognition. In: Proceedings of Asia–Pacific signal and information processing association annual summit conference (APSIPA ASC’2011), paper Wed-AM.RS6

  6. Sharma A, Dubey A, Tripathi P, Kumar V (2010) Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces. Neurocomputing 73(10–12):1868–1880

    Article  Google Scholar 

  7. Xu Y, Zhu X, Li Z, Liu G, Lu Y, Liu H (2013) Using the original and ’symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recogn 46(4):1151–1158

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Tang D, Zhu N, Yu F, et al. (2014) A novel sparse representation method based on virtual samples for face recognition. Neural Comput Appl 24:513–519

    Article  Google Scholar 

  10. Sanderson C, Paliwal KK (2003) Noise compensation in a person verification system using face and multiple speech feature. Pattern Recogn 36:293–302

    Article  Google Scholar 

  11. Peng WC, Bing SH, She ZJ et al (2017) Robust face recognition via discriminative and common hybrid dictionary learning[J]. Applied Intelligence 2017(5500):1–10

    Google Scholar 

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

    Article  Google Scholar 

  13. Lin G, Yang M, Yang J, Shen L, Xie W (2018) Robust, discriminative and comprehensive dictionary learning for face recognition. Pattern Recogn 81:341–356

    Article  Google Scholar 

  14. Luo X, Xu Y, Yang J (2019) Multi-resolution dictionary learning for face recognition. Pattern Recog 93:283–292

    Article  Google Scholar 

  15. Xu Y, Zhong Z, Yang J, You J, Zhang D (2017) A new discriminative sparse representation method for robust face recognition via l(2) regularization. IEEE Trans Neural Netw Learn Syst 28(10):2233–2242

    Article  MathSciNet  Google Scholar 

  16. Zhang H, Zhang Y, Huang TS (2013) Pose-robust face recognition via sparse representation. Pattern Recogn 46(5):1511–1521

    Article  Google Scholar 

  17. Yang J, Chu D, Zhang L, Xu Y, Yang J (J2013) Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Trans Neural Netw Learn Syst 24(7):1023–1035

  18. Gao S, Chia L-T, Tsang IW-H (2011) Multi-layer group sparse coding—For concurrent image classification and annotation, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 2809–2816

  19. Yang J, Zhang L, Xu Y, Yang J-Y (2012) Beyond sparsity: The role of L1-optimizer in pattern classification. Pattern Recogn 45(3):1104–1118

    Article  Google Scholar 

  20. Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 1–8

  21. Jing G, Shi Y, Kong D, Ding W, Yin B (2014) Image super-resolution based on multi-space sparse representation. Multimedia Tools Appl 70(2):741–755

    Article  Google Scholar 

  22. Li H, He X, Tao D, Tang Y, Wang R (2018) Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning. Pattern Recogn 79:130–146

    Article  Google Scholar 

  23. Wagner A, Wright J, Ganesh A, Zhou Z, Mobahi H, Ma Y (2012) Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386

    Article  Google Scholar 

  24. Rubinstein R, Bruckstein AM, Elad M (2010) Dictionaries for sparse representation modeling. Proc IEEE 98(6):1045–1057

    Article  Google Scholar 

  25. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: Proc. Int Conf. Comput. Vis. (ICCV), pp 543–550

  26. Li Z, Lai Z, Xu Y, Yang J, Zhang D A locality-constrained and label embedding dictionary learning algorithm for image classification, IEEE Trans. Neural Netw. Learn. Syst., to be published. https://doi.org/10.1109/TNNLS.2015.2508025

  27. Mazhar R, Gader PD (2008) EK-SVD: Optimized dictionary design for sparse representations. In: Proc. IEEE Conf. Pattern Recognit. (ICPR), pp 1–4

  28. 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Liu Z, Pu J, Huang T, Qiu Y (2013) A novel classification method for palmprint recognition based on reconstruction error and normalized distance. Appl Intell 39(2):307–314

    Article  Google Scholar 

  31. Needell D, Vershynin R (2010) Signal recovery from inaccurate and incomplete measurements via regularized orthogonal matching pursuit. IEEE J Sel Topics Signal Process 4(2):310– 316

    Article  Google Scholar 

  32. Boyd SP (2008) https://web.stanford.edu/∼boyd/l1_ls/, (accessed 04.08)

  33. Yang AY, Zhou Z, Balasubramanian AG, Sastry SS, Ma Y (2013) Fast ’1-minimization algorithms for robust face recognition. IEEE Trans Image Process 22(8):3234–3246

    Article  Google Scholar 

  34. Gong P-H, Zhang C-S, Lu Z-S, Huang J-H, Ye J-P (2013) A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems. In: Proceedings of international conference on machine learning, ICML, pp 37–45

  35. Xu Y, Zhang D, Yang J, Yang J-Y (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262

    Article  MathSciNet  Google Scholar 

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

  37. Xu Y, Zhang B, Zhong Z (2015) Multiple representations and sparse representation for image classification. Pattern Recogn Lett 68:9–14. https://doi.org/10.1016/j.patrec.2015.07.032

    Article  Google Scholar 

  38. Wen J, Fang X, Cui J, Fei L, Yan K, Chen Y, Xu Y (2019) Robust Sparse Linear Discriminant Analysis. IEEE Trans Circuits Syst Video Technol 29(2):390–403

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  40. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of 1994 IEEE workshop on applications of computer vision, IEEE Comput. Soc. Press, pp 138–142

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

    Article  Google Scholar 

  42. Fang XZ, Xu Y, Li XL, Lai ZH, Wong WK (2015) Learning a nonnegative sparse graph for linear regression. IEEE Trans Image Process 24(9):2760–2771

    Article  MathSciNet  Google Scholar 

  43. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104

    Article  Google Scholar 

  44. Phillips PJ The facial recognition technology (FERET) database [Online]. Available: https://www.nist.gov/programs-projects/face-recognition-technology-feret

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China

    Shijun Zheng, Yongjun Zhang, Wenjie Liu & Yongjie Zou

Authors
  1. Shijun Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongjun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenjie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongjie Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongjun Zhang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, S., Zhang, Y., Liu, W. et al. Improved image representation and sparse representation for image classification. Appl Intell 50, 1687–1698 (2020). https://doi.org/10.1007/s10489-019-01612-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-019-01612-3

Keywords

Search

Navigation