Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 21, pp 30175–30196 | Cite as

Domain adaptive collaborative representation based classification

  • Guoqing ZhangEmail author
  • Yuhui Zheng
  • Guiyu Xia
Article
  • 118 Downloads

Abstract

Conventional representation based classification methods, such as sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been developed and shown great potential due to its effectiveness in various recognition tasks. However, when the test data and training data come from different distribution, the performance of SRC and CRC will be degraded significantly. Recently, several sparse representation based domain adaptation learning (DAL) methods have been proposed and achieve impressive performance. However, these sparse representation based DAL methods need to solve the 1-norm optimization problem, which is extremely time-consuming. To address this problem, in this paper, we propose a simple yet much more efficient domain adaptive collaborative representation-based classification method (DACRC). By replacing the 2-norm regularization term using the 2-norm, we exploit the collaborative representation rather than sparse representation to jointly learn projections of data in the two domains. In addition, a common dictionary is also learned such that in the projected space the learned dictionary can optimal represent both training and test data. Furthermore, the proposed method is effective to deal with multiple domains problem and is easy to kernelized. Compared with other sparse representation based DAL methods, DACRC is computationally efficient and its performance is better or comparable to many state-of-the-art methods.

Keywords

Domain adaptation Collaborative representation Joint projection and dictionary learning Non-linear representation 

Notes

References

  1. 1.
    Ben-David S, Blitzer J, Crammer K, Pereira F (2007) Analysis of representation for domain adaptation. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 137–144Google Scholar
  2. 2.
    Bibi A, Ghanem B (2017) High order tensor formulation for convolutional sparse coding. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 1772–1780Google Scholar
  3. 3.
    Brian K, Saenko K, Darrell T (2011) What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 1785–1792Google Scholar
  4. 4.
    Cai S, Zhang L, Zuo W, Feng X (2016) A probabilistic collaborative representation based approach for pattern classification, in: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 5839–5847Google Scholar
  5. 5.
    Courty N, Flamary R, Tuia D, Rakotomamonjy A (2017) Optimal transport for domain adaptation. IEEE Trans Pattern Anal Mach Intell 39(9):1853–1865CrossRefGoogle Scholar
  6. 6.
    Daume H III (2007) Frustratingly easy domain adaptation. In: Proceedings of the of ACL, pp. 256–263Google Scholar
  7. 7.
    Deng W, Hu J, Guo J (2017) Face recognition via collaborative representation: its discriminant nature and superposed representation. IEEE Trans Pattern Anal Mach IntellGoogle Scholar
  8. 8.
    Gao Y, Ma J, Yuille AL (2017) Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans Image Processing 26(5):2545–2560MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gebru T, Hoffman J, Fei-Fei L (2017) Fine-grained recognition in the wild: a multi-task domain adaptation approach. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (ICCV), pp. 1349–1358Google Scholar
  10. 10.
    Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (ICCV), 2066–2073Google Scholar
  11. 11.
    Gopalan R, Li R, Chellappa R (2011) Domain adaptation for object recognition: An unsupervised approach. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 999–1006Google Scholar
  12. 12.
    Griffin G, Holub A, Perona P (2007) Caltech-256 object category data set, CIT Technical Report 7694Google Scholar
  13. 13.
    Huang W, Harandi M, Zhang T, Fan L (2017) Efficient optimization for linear dynamical systems with applications to clustering and sparse coding, in: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 3447–3457Google Scholar
  14. 14.
    Huang W, Sun F, Cao L, Zhao D, Liu H, Harandi M (2016) Sparse coding and dictionary learning with linear dynamical system. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 3938–3947Google Scholar
  15. 15.
    Jiang X, Lai J (2015) Sparse and dense hybrid representation via dictionary decomposition for face recognition. IEEE Trans Pattern Anal Mach Intell 37(5):1067–1079CrossRefGoogle Scholar
  16. 16.
    Lu B, Chellapp A, Nasrabadi N (2015) Incremental dictionary learning for unsupervised domain adaptation. In: Proceedings of the British Machine Vision Conference (BMVC). pp. 108.1–108.2Google Scholar
  17. 17.
    Lu J, Wang G, Zhou J (2017) Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans Image Processing 26(8):4042–4054MathSciNetCrossRefGoogle Scholar
  18. 18.
    Monga V, Damera-Venkata N, Rehman H, Evans BL (2005) Halftoning toolbox for MATLAB. Available: http://users.ece.utexas.edu/bevans/projects/halftoning/toolbox/
  19. 19.
    Ni J, Qiu Q, Chellappa R (2017) Subspace interpolation via dictionary learning for unsupervised domain adaptation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (ICCV), pp. 692–699Google Scholar
  20. 20.
    Pan S, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl & Data Eng 22(10):1345–1359CrossRefGoogle Scholar
  21. 21.
    Patel VM, Gopalan R, Li R, Chellappa R (2015) Visual domain adaptation: A survey of recent advances. IEEE Signal Process Mag 32(3):53–69CrossRefGoogle Scholar
  22. 22.
    Qi Y, Qin L, Zhang J, Zhang S, Huang Q, Yang MH (2018) Structure-aware local sparse coding for visual tracking. IEEE Trans Image Processing 27(8):3857–3869MathSciNetCrossRefGoogle Scholar
  23. 23.
    Qiu Q, Patel VM, Turaga P, Chellappa R (2012) Domain adaptive dictionary learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 631–645CrossRefGoogle Scholar
  24. 24.
    Quan Y, Xu Y, Sun Y, Huang Y, Ji H (2016) Sparse coding for classification via discriminative ensemble. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 5839–5847Google Scholar
  25. 25.
    Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 213–226CrossRefGoogle Scholar
  26. 26.
    Shekhar S, Patel VM, Nguyen HV, Chellappa R (2013) Generalized domain-adaptive dictionaries. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 361–368Google Scholar
  27. 27.
    Shekhar S, Patel VM, Nguyen HV, Chellappa R (2015) Couple projections for adaptation of dictionaries. IEEE Trans Image Process 24(10):2941–2954MathSciNetCrossRefGoogle Scholar
  28. 28.
    Shi Y, Sha F (2012) Information-theoretical learning of discriminative adaption. In: Proceedings of the International Conference on Machine Learning, (ICML) pp. 1079–1086Google Scholar
  29. 29.
    Shrivastava A, Shekhar S, Patel VM (2014) Unsupervised domain adaptation using parallel transport on Grassmann manifold. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 277–284Google Scholar
  30. 30.
    Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 7167–7176Google Scholar
  31. 31.
    Wen Z, Yin W (2013) A feasible method for optimization with orthogonality constraints. Math Program 142(1–2):397–434MathSciNetCrossRefGoogle Scholar
  32. 32.
    Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRefGoogle Scholar
  33. 33.
    Xia Z, Ma X, Shen Z, Sun X, Xiong N, Jeon B (2018) Secure image LBP feature extraction in cloud-based smart campus. IEEE Access 6(1):30392–30401CrossRefGoogle Scholar
  34. 34.
    Xiong N, Vasilakos AV, Yang LT, Song L, Pan Y, Kannan R, Li Y (2009) Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE Journal on Selected Areas in Communications 27(4):495–509CrossRefGoogle Scholar
  35. 35.
    Yang M, Chen L (2017) Discriminative semi-supervised dictionary learning with entropy regularization for pattern classification. In: Proceedings of AAAI conf. Aritificial Intell, pp. 1626–1632Google Scholar
  36. 36.
    Yang J, Wright J, Ma Y, Huang T (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873MathSciNetCrossRefGoogle Scholar
  37. 37.
    Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition. In: Proceedings of the International Conference on Image Processing (ICIP), pp. 1601–1604Google Scholar
  38. 38.
    Zhang H, Patel VM, Shekhar S, Chellappa R (2015) Domain-adaptive sparse representation-based classification, in: Proceedings of the Automatic Face and Gesture Recognition Workshops, pp. 1–8Google Scholar
  39. 39.
    Zhang G, Sun H, Ji Z, Sun Q (2016) Label propagation based on collaborative representation for face recognition. Neurocomputing 171:1193–1204CrossRefGoogle Scholar
  40. 40.
    Zhang G, Sun H, Ji Z, Yuan Y, Sun Q (2016) Cost-sensitive dictionary learning for face recognition. Pattern Recogn 60:613–629CrossRefGoogle Scholar
  41. 41.
    Zhang G, Sun H, Porikli F, Liu Y, Sun Q (2017) Optimal couple projections for domain adaptive sparse representation-based classification. IEEE Trans Image Process 26(12):5922–5935MathSciNetCrossRefGoogle Scholar
  42. 42.
    Zhang G, Sun H, Xia G, Feng L, Sun Q (2016) Kernel dictionary learning based discriminant analysis. J Vis Commun Image Represent 40:470–484CrossRefGoogle Scholar
  43. 43.
    Zhang G, Sun H, Xia G, Sun Q (2016) Multiple kernel sparse representation based orthogonal discriminative projection and its cost-sensitive extension. IEEE Trans Image Processing 25(9):4271–4285MathSciNetzbMATHGoogle Scholar
  44. 44.
    Zhang G, Sun H, Xia G, Sun Q (2016) Kernel collaborative representation based dictionary learning and discriminative projection. Neurocomputing 207:300–309CrossRefGoogle Scholar
  45. 45.
    Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 471–478Google Scholar
  46. 46.
    Zhang L, Zhou W, Chang P, Liu J, Yan Z, Wang T, Li F (2012) Kernel sparse representation-based classifier. IEEE Trans Signal Process 60(4):1684–1695MathSciNetCrossRefGoogle Scholar
  47. 47.
    Zheng H, Guo W, Xiong N (2017) A kernel-based compressive sensing approach for mobile data gathering in wireless sensor network systems. IEEE Trans Systems, Man and Cybernetics: System 8(9):1–13Google Scholar
  48. 48.
    Zhu X, Li X, Zhang S, Ju C, Wu X (2017) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans Neural Netw Learn Syst 28(6):1263–1275MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  2. 2.School of Information and ControlNanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations