Skip to main content
SpringerLink
Log in

Multi-view Discriminant Dictionary Learning via Learning View-specific and Shared Structured Dictionaries for Image Classification

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Recently, multi-view dictionary learning technique has attracted lots of research interest. Although some multi-view dictionary learning methods have been addressed, there exists much room for improvement. How to explore and utilize both the diversity and the useful correlation information of different views with dictionaries has not been well studied. In this paper, we propose a novel multi-view dictionary learning approach named multi-view discriminant dictionary learning via learning view-specific and shared structured dictionaries (MDVSD), which aims to learn a structured dictionary shared by all views and multiple view-specific structured dictionaries with each corresponding to a specific view. The shared dictionary is combined with each view-specific dictionary to represent data of the specific view. MDVSD makes the view-specific dictionaries corresponding to different views uncorrelated for effectively exploring the diversity of different views. Furthermore, we introduce structural uncorrelation into shared dictionary learning procedure, such that the useful correlation information of different views can be effectively exploited. Dictionary-atoms in shared and view-specific dictionaries have correspondence to class labels so that the learned dictionaries have favorable discriminant ability and the obtained reconstruction error is discriminative. Three widely used datasets are employed as test data. Experimental results demonstrate the effectiveness of the proposed approach.

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

Similar content being viewed by others

References

  1. Memisevic R (2012) On multi-view feature learning. In: International conference on machine learning (ICML), pp 161–168

  2. Kumar A, Daumé H (2011) A co-training approach for multi-view spectral clustering. In: International conference on machine learning (ICML), pp 393–400

  3. Kumar A, Rai P, Daumé H (2011) Co-regularized multi-view spectral clustering. In: Advances in neural information processing systems (NIPS), pp 1413–1421

  4. Gönen M, Alpaydın E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211–2268

    MathSciNet  MATH  Google Scholar 

  5. Kloft M, Brefeld U, Sonnenburg S, Zien A (2011) Lp-norm multiple kernel learning. J Mach Learn Res 12:953–997

    MathSciNet  MATH  Google Scholar 

  6. Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038

    Article  Google Scholar 

  7. Xu C, Tao D, Xu C (2013) A survey on multi-view learning. arXiv preprint arXiv:1304.5634

  8. Gao L, Qi L, Chen E, Guan L (2012) Discriminative multiple canonical correlation analysis for multi-feature information fusion. In: IEEE international symposium on multimedia, pp 36–43

  9. Shen X, Sun Q (2015) Orthogonal multiset canonical correlation analysis based on fractional-order and its application in multiple feature extraction and recognition. Neural Process Lett 42(2):301–316

    Article  Google Scholar 

  10. Yuan YH, Sun QS, Ge HW (2014) Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition. Pattern Recogn 47(3):1411–1424

    Article  MATH  Google Scholar 

  11. Li YO, Adali T, Wang W, Calhoun VD (2009) Joint blind source separation by multiset canonical correlation analysis. IEEE Trans Signal Process 57(10):3918–3929

    Article  MathSciNet  Google Scholar 

  12. Jing X, Hu R, Zhu Y, Wu S, Liang C, Yang J (2014). Intra-view and inter-view supervised correlation analysis for multi-view feature learning. In: AAAI conference on artificial intelligence (AAAI), pp 1882–1889

  13. Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. In IEEE conference on computer vision and pattern recognition (CVPR), pp 2160–2167

  14. Diethe T, Hardoon DR, Shawe-Taylor J (2008) Multiview fisher discriminant analysis. In: NIPS workshop on learning from multiple sources

  15. Kan M, Shan S, Zhang H, Lao S, Chen X (2012) Multi-view discriminant analysis. In European conference on computer vision (ECCV), pp 808–821

  16. Sun S, Xie X, Yang M (2015) Multiview uncorrelated discriminant analysis. IEEE Trans Cybern (in press)

  17. Shekhar S, Patel VM, Nasrabadi NM, Chellappa R (2014) Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans Pattern Anal Mach Intell 36(1):113–126

    Article  Google Scholar 

  18. Jia Y, Salzmann M, Darrell T (2010) Factorized latent spaces with structured sparsity. In: Advances in neural information processing systems (NIPS), pp 982–990

  19. Zheng S, Xie B, Huang K, Tao D (2011) Multi-view pedestrian recognition using shared dictionary learning with group sparsity. In: International conference on neural information processing (ICONIP), pp 629–638

  20. Zheng J, Jiang Z, Phillips PJ, Chellappa R (2012) Cross-view action recognition via a transferable dictionary pair. In: British machine vision conference (BMVC)

  21. Gao Z, Zhang H, Xu GP, Xue YB, Hauptmann AG (2014) Multi-view discriminative and structured dictionary learning with group sparsity for human action recognition. Signal Process 112:83–97

    Article  Google Scholar 

  22. Zhuang Y, Wang Y, Wu F, Zhang Y, Lu W (2013) Supervised coupled dictionary learning with group structures for multi-modal retrieval. In AAAI conference on artificial intelligence (AAAI), pp 1070–1076

  23. Patel VM, Gopalan R, Li R, Chellappa R (2015) Visual domain adaptation: a survey of recent advances. IEEE Signal Process Mag 32(3):53–69

    Article  Google Scholar 

  24. Shekhar S, Patel VM, Nguyen HV, Chellappa R (2013) Generalized domain-adaptive dictionaries. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 361–368

  25. Zhang H, Nasrabadi NM, Zhang Y, Huang TS (2012) Joint dynamic sparse representation for multi-view face recognition. Pattern Recogn 45(4):1290–1298

    Article  Google Scholar 

  26. Zheng J, Jiang Z (2013) Learning view-invariant sparse representations for cross-view action recognition. In: IEEE conference on computer vision (ICCV), pp 3176–3183

  27. Shi Y, Gao Y, Yang Y, Zhang Y, Wang D (2013) Multi-modal sparse representation-based classification for lung needle biopsy images. IEEE Trans Biomed Eng 60(10):2675–2685

    Article  Google Scholar 

  28. Jing X, Hu R, Wu F, Chen X, Liu Q, Yao Y (2014) Uncorrelated multi-view discrimination dictionary learning for recognition. In: AAAI conference on artificial intelligence (AAAI), pp 2787–2795

  29. Gao S, Tsang IW, Ma Y (2014) Learning category-specific dictionary and shared dictionary for fine-grained image categorization. IEEE Trans Image Process 23(2):623–634

    Article  MathSciNet  Google Scholar 

  30. Wang D, Kong S (2014) A classification-oriented dictionary learning model: explicitly learning the particularity and commonality across categories. Pattern Recogn 47(2):885–898

    Article  MATH  Google Scholar 

  31. Cai D, He X, Han J, Zhang HJ (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614

    Article  Google Scholar 

  32. Murase H, Nayar SK (1995) Visual learning and recognition of 3-D objects from appearance. Int J Comput Vis 14(1):5–24

    Article  Google Scholar 

  33. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  34. Chen CF, Wei CP, Wang YC (2012) Low-rank matrix recovery with structural incoherence for robust face recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2618–2625

  35. Rosasco L, Verri A, Santoro M, Mosci S, Villa S (2009) Iterative projection methods for structured sparsity regularization. MIT Technical Reports, MIT-CSAIL-TR-2009-050, CBCL-282, Massachusetts Institute of Technology

  36. 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 

  37. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3(1):71–86

    Article  Google Scholar 

  38. Grigorescu SE, Petkov N, Kruizinga P (2002) Comparison of texture features based on Gabor filters. IEEE Trans Image Process 11(10):1160–1167

    Article  MathSciNet  Google Scholar 

  39. Fukunaga K, Koontz WL (1970) Application of the Karhunen–Loeve expansion to feature selection and ordering. IEEE Trans Comput 19(4):311–318

    Article  MATH  Google Scholar 

  40. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  MATH  Google Scholar 

  41. Draper BA, Yambor WS, Beveridge JR (2002) Analyzing PCA-based face recognition algorithms: eigenvector selection and distance measures. In: IEEE workshop empirical evaluation methods in computer vision, pp 1–15

Download references

Acknowledgments

The authors want to thank the anonymous reviewers for their constructive comments and suggestions. The work described in this paper was fully supported by the National Natural Science Foundation of China under Projects No. 61272273, No. 61502245, and No. 61533010.

Author information

Authors and Affiliations

  1. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Fei Wu, Xiao-Yuan Jing & Dong Yue

  2. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Xiao-Yuan Jing

Authors
  1. Fei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-Yuan Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dong Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao-Yuan Jing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, F., Jing, XY. & Yue, D. Multi-view Discriminant Dictionary Learning via Learning View-specific and Shared Structured Dictionaries for Image Classification. Neural Process Lett 45, 649–666 (2017). https://doi.org/10.1007/s11063-016-9545-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-016-9545-7

Keywords

Search

Navigation