Advertisement

Sparse representation for image classification via paired dictionary learning

  • Hui-Hung Wang
  • Chia-Wei Tu
  • Chen-Kuo ChiangEmail author
Article
  • 49 Downloads

Abstract

Sparse coding technique is usually applied for feature representation. To learn discriminative features for visual recognition, a dictionary learning method, called Paired Discriminative K-SVD (PD-KSVD), is presented in this paper. Firstly, to reduce the reconstruction error of positive class while increasing the errors of negative classes, the scheme inverted signal is applied to the negative training samples. Then, the class-specific sub-dictionaries are learned from pairs of positive and negative classes to jointly achieve high discrimination and low reconstruction errors for sparse coding. Multiple sub-dictionaries are concatenated with respect to the same negative class so that the non-zero sparse coefficients can be discriminatively distributed to improve classification accuracy. Last, sparse coefficients are solved via the concatenated sub-dictionaries and used to train the classifier. Compared to the existing dictionary learning methods, PD-KSVD method achieves superior performance in a variety of visual recognition tasks on several publicly available datasets.

Keywords

Dictionary learning Sparse coding Visual recognition 

Notes

References

  1. 1.
    Aharon M, Elad M, Bruckstein A (2006) rmk-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54 (11):4311–4322CrossRefGoogle Scholar
  2. 2.
    Bao C, Cai JF, Ji H (2013) Fast sparsity-based orthogonal dictionary learning for image restoration. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 3384–3391Google Scholar
  3. 3.
    Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Dis 2(2):121–167CrossRefGoogle Scholar
  4. 4.
    Cai S, Zuo W, Zhang L, Feng X, Wang P (2014) Support vector guided dictionary learning. In: European Conference on Computer Vision. Springer, pp 624–639Google Scholar
  5. 5.
    Castrodad A, Sapiro G (2012) Sparse modeling of human actions from motion imagery. Int J Comput Vis 100(1):1–15CrossRefGoogle Scholar
  6. 6.
    Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3):27Google Scholar
  7. 7.
    Deka B, Gorain KK, Kalita N, Das B (2013) Single image super-resolution using compressive sensing with learned overcomplete dictionary. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, pp 1–5Google Scholar
  8. 8.
    Engan K, Aase SO, Husoy J (1999) Frame based signal compression using method of optimal directions (mod). In: 1999 Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, ISCAS’99, vol 4. IEEE, pp 1–4Google Scholar
  9. 9.
    Feng Z, Yang M, Zhang L, Liu Y, Zhang D (2013) Joint discriminative dimensionality reduction and dictionary learning for face recognition. Pattern Recogn 46(8):2134–2143CrossRefGoogle Scholar
  10. 10.
    Griffin G, Holub A, Perona P (2007) Caltech-256 object category datasetGoogle Scholar
  11. 11.
    Gu S, Zhang L, Zuo W, Feng X (2014) Projective dictionary pair learning for pattern classification. In: Advances in neural information processing systems, pp 793–801Google Scholar
  12. 12.
    Huang DA, Wang YCF (2013) Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 2496–2503Google Scholar
  13. 13.
    Hull JJ (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16(5):550–554CrossRefGoogle Scholar
  14. 14.
    Jiang Z, Lin Z, Davis LS (2011) Learning a discriminative dictionary for sparse coding via label consistent k-svd. In: 2011 IEEE Conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1697–1704Google Scholar
  15. 15.
    Li FF, Fergus R, Perona P (2007) Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput Vis Image Understand 106(1):59–70CrossRefGoogle Scholar
  16. 16.
    Lu H (2013) Learning canonical correlations of paired tensor sets via tensor-to-vector projectionGoogle Scholar
  17. 17.
    Lu C, Shi J, Jia J (2013) Online robust dictionary learning. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 415–422Google Scholar
  18. 18.
    Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2008) Discriminative learned dictionaries for local image analysis. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition CVPR. IEEE, pp 1–8Google Scholar
  19. 19.
    Nie L, Wang X, Zhang J, He X, Zhang H, Hong R, Tian Q (2017) Enhancing micro-video understanding by harnessing external sounds. In: Proceedings of the 2017 ACM on Multimedia Conference, MM 2017, Mountain View, CA, USA, October 23-27, 2017, pp 1192–1200.  https://doi.org/10.1145/3123266.3123313
  20. 20.
    Nie L, Wei X, Zhang D, Wang X, Gao Z, Yang Y (2017) Data-driven answer selection in community QA systems. IEEE Trans Knowl Data Eng 29(6):1186–1198.  https://doi.org/10.1109/TKDE.2017.2669982 CrossRefGoogle Scholar
  21. 21.
    Pati YC, Rezaiifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: 1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, systems and computers. IEEE, pp 40–44Google Scholar
  22. 22.
    Peng Y, Meng D, Xu Z, Gao C, Yang Y, Zhang B (2014) Decomposable nonlocal tensor dictionary learning for multispectral image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2949–2956Google Scholar
  23. 23.
    Quattoni A, Torralba A (2009) Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition CVPR. IEEE, pp 413–420Google Scholar
  24. 24.
    Ramirez I, Sprechmann P, Sapiro G (2010) Classification and clustering via dictionary learning with structured incoherence and shared features. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3501–3508Google Scholar
  25. 25.
    Rubinstein R, Peleg T, Elad M (2013) Analysis k-svd: a dictionary-learning algorithm for the analysis sparse model. IEEE Trans Signal Process 61(3):661–677MathSciNetCrossRefGoogle Scholar
  26. 26.
    Said AB, Jemel I, Ejbali R, Zaied M (2017) A hybrid approach for image classification based on sparse coding and wavelet decomposition. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp 63–68.  https://doi.org/10.1109/AICCSA.2017.117
  27. 27.
    Shen L, Wang S, Sun G, Jiang S, Huang Q (2013) Multi-level discriminative dictionary learning towards hierarchical visual categorization. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 383–390Google Scholar
  28. 28.
    Sun Y, Liu Q, Tang J, Tao D (2014) Learning discriminative dictionary for group sparse representation. IEEE Trans Image Process 23(9):3816–3828MathSciNetCrossRefGoogle Scholar
  29. 29.
    Sun X, Nasrabadi NM, Tran TD (2018) Supervised deep sparse coding networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp 346–350.  https://doi.org/10.1109/ICIP.2018.8451701
  30. 30.
    Toh SH, Prathipati P, Motakis E, Kwoh CK, Yenamandra SP, Kuznetsov VA (2011) A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma. In: BMC Genomics, biomed central, vol 12, p S24Google Scholar
  31. 31.
    Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3360–3367Google Scholar
  32. 32.
    Wang S, Zhang L, Liang Y, Pan Q (2012) Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol 2012. IEEE, pp 2216–2223Google Scholar
  33. 33.
    Wang HH, Chen YL, Chiang CK (2016) Discriminative paired dictionary learning for visual recognition. In: Proceedings of the 2016 ACM on Multimedia Conference. ACM, pp 67–71Google Scholar
  34. 34.
    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):210–227CrossRefGoogle Scholar
  35. 35.
    Xing J, Gao J, Li B, Hu W, Yan S (2013) Robust object tracking with online multi-lifespan dictionary learning. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 665–672Google Scholar
  36. 36.
    Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 543–550Google Scholar
  37. 37.
    Yang M, Van Gool L, Zhang L (2013) Sparse variation dictionary learning for face recognition with a single training sample per person. In: 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 689–696Google Scholar
  38. 38.
    Yang M, Dai D, Shen L, Van Gool L (2014) Latent dictionary learning for sparse representation based classification. In: Proceedings CVPR, vol 2014, pp 4138–4145Google Scholar
  39. 39.
    Zhang Q, Li B (2010) Discriminative k-svd for dictionary learning in face recognition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 2691–2698Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Innovative Research on Aging Society (CIRAS), Advanced Institute of Manufacturing with High-tech Innovations and Department of Computer Science and Information EngineeringNational Chung Cheng UniversityChiayiTaiwan

Personalised recommendations