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Image classification via convolutional sparse coding

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Abstract

The Convolutional Sparse Coding (CSC) model has recently attracted a lot of attention in the signal and image processing communities. Since, in traditional sparse coding methods, a significant assumption is that all input samples are independent, so it is not well for most dependent works. In such cases, CSC models are a good choice. In this paper, we proposed a novel CSC-based classification model which combines the local block coordinate descent (LoBCoD) algorithm with the classification strategy. For this, in the training phase, the convolutional dictionary atoms (filters) of each class are learned by all training samples of the same class. In the test phase, the label of the query sample can be determined based on the reconstruction error of the filters related to every subject. Experimental results on five benchmark databases at the different number of training samples clearly demonstrate the superiority of our method to many state-of-the-art classification methods. Besides, we have shown that our method is less dependent on the number of training samples and therefore it can better work than other methods in small databases with fewer samples. For instance, increases of 26.27%, 18.32%, 11.35%, 13.5%, and 19.3% in recognition rates are observed for our method when compared to conventional SRC for five used databases at the least number of training samples per class.

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References

  1. Bai, X., Zhang, T., Wang, C., Abd El-Latif, A.A., Niu, X.: A fully automatic player detection method based on one-class svm. IEICE Trans. Inf. Syst. 96(2), 387–391 (2013)

    Article  Google Scholar 

  2. Benrhouma, O., Hermassi, H., Abd El-Latif, A.A., Belghith, S.: Chaotic watermark for blind forgery detection in images. Multimed. Tools Appl. 75(14), 8695–8718 (2016)

    Article  Google Scholar 

  3. Aharon, M., Elad, M., Bruckstein, A.: K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    Article  MATH  Google Scholar 

  4. Lahaw, Z.B., Seddik, H.: A new greedy sparse recovery algorithm for fast solving sparse representation. Vis. Comput. 1–15 (2021)

  5. Yang, J., Tse, P.: Sparse representation of complex steerable pyramid for machine fault diagnosis by using non-contact video motion to replace conventional accelerometers. Measurement 175, 109104 (2021)

    Article  Google Scholar 

  6. Gou, J., Wang, L., Yi, Z., Yuan, Y., Ou, W., Mao, Q.: Weighted discriminative collaborative competitive representation for robust image classification. Neural Netw. 125, 104–120 (2020)

    Article  Google Scholar 

  7. Zheng, H., Tao, D.: Discriminative dictionary learning via fisher discrimination k-svd algorithm. Neurocomputing 162, 9–15 (2015)

    Article  Google Scholar 

  8. Li, Z., Zhang, Z., Qin, J., Zhang, Z., Shao, L.: Discriminative fisher embedding dictionary learning algorithm for object recognition. IEEE Trans. Neural Netw. Learn. Syst. 31(3), 786–800 (2019)

    Article  MathSciNet  Google Scholar 

  9. Mokhayeri, F., Granger, E.: A paired sparse representation model for robust face recognition from a single sample. Pattern Recognit. 100, 107129 (2020)

    Article  Google Scholar 

  10. Zeng, S., Zhang, B., Gou, J., Xu, Y.: Regularization on augmented data to diversify sparse representation for robust image classification. IEEE Trans. Cybern. (2020)

  11. Nozaripour, A., Soltanizadeh, H.: Robust vein recognition against rotation using kernel sparse representation. J. AI Data Min. (2021)

  12. Foroughi, H., Ray, N., Zhang, H.: Object classification with joint projection and low-rank dictionary learning. IEEE Trans. Image Process. 27(2), 806–821 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  13. Hu, J., Tan, Y.-P.: Nonlinear dictionary learning with application to image classification. Pattern Recognit. 75, 282–291 (2018)

    Article  Google Scholar 

  14. Wang, B., Guo, J., Zhang, Y., Li, C.: Hierarchical feature concatenation-based kernel sparse representations for image categorization. Vis. Comput. 33(5), 647–663 (2017)

    Article  Google Scholar 

  15. Yang, M., Zhang, L., Yang, J., Zhang, D.: Metaface learning for sparse representation based face recognition. In: 2010 IEEE International Conference on Image Processing. IEEE, pp. 1601–1604 (2010)

  16. Wang, X., Li, H., Qiu, J., Yu, C.: Palm vein recognition based on competitive code and dpl. In: Proceedings of the 3rd International Conference on Cryptography, Security and Privacy, pp. 179–183 (2019)

  17. Liu, L., Chen, S., Chen, X., Wang, T., Zhang, L.: Fuzzy weighted sparse reconstruction error-steered semi-supervised learning for face recognition. Vis. Comput. 36(8), 1521–1534 (2020)

    Article  Google Scholar 

  18. An, F.: Image classification algorithm based on stacked sparse coding deep learning model-optimized kernel function nonnegative sparse representation. Soft. Comput. 24(22), 16967–16981 (2020)

    Article  Google Scholar 

  19. Zha, Z., Liu, X., Zhang, X., Chen, Y., Tang, L., Bai, Y., Wang, Q., Shang, Z.: Compressed sensing image reconstruction via adaptive sparse nonlocal regularization. Vis. Comput. 34(1), 117–137 (2018)

    Article  Google Scholar 

  20. Parvasideh, P., Rezghi, M.: A novel dictionary learning method based on total least squares approach with application in high dimensional biological data. In: Advances in Data Analysis and Classification, pp. 1–23 (2020)

  21. Gai, S.: Color image denoising via monogenic matrix-based sparse representation. Vis. Comput. 35(1), 109–122 (2019)

    Article  MathSciNet  Google Scholar 

  22. Ding, Z., Shao, M., Fu, Y.: Deep robust encoder through locality preserving low-rank dictionary. In: European Conference on Computer Vision. Springer, pp. 567–582 (2016)

  23. Fan, C., Hu, C., Liu, B.: Linearized kernel dictionary learning with group sparse priors for action recognition. Vis. Comput. 35(12), 1797–1807 (2019)

    Google Scholar 

  24. Xu, Y., Li, Z., Yang, J., Zhang, D.: A survey of dictionary learning algorithms for face recognition. IEEE Access 5, 8502–8514 (2017)

    Article  Google Scholar 

  25. Shazeeda, S., Rosdi, B.A.: Finger vein recognition using mutual sparse representation classification. IET Biometrics 8(1), 49–58 (2018)

    Article  Google Scholar 

  26. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2008)

    Article  Google Scholar 

  27. Yang, M., Zhang, L., Feng, X., Zhang, D.: Sparse representation based fisher discrimination dictionary learning for image classification. Int. J. Comput. Vis. 109(3), 209–232 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  28. Zhao, Z., Shen, Q., Feng, G., Zhu, J.: Collaborative coding and dictionary learning for nearest subspace classification. Soft. Comput. 25(11), 7627–7643 (2021)

    Article  Google Scholar 

  29. Zhao, L., Zhang, Y., Yin, B., Sun, Y., Hu, Y., Piao, X., Wu, Q.: Fisher discrimination-based \(\ell _{2,1}\)-norm sparse representation for face recognition. Vis. Comput. 32(9), 1165–1178 (2016)

    Article  Google Scholar 

  30. Liu, Z., Wu, X.-J., Shu, Z.: Sparsity augmented discriminative sparse representation for face recognition. Pattern Anal. Appl. 22(4), 1527–1535 (2019)

    Article  MathSciNet  Google Scholar 

  31. Jin, J., Chen, C.P.: Convolutional sparse coding for face recognition. In: 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS). IEEE, pp. 137–141 (2017)

  32. Zisselman, E., Sulam, J., Elad, M.: A local block coordinate descent algorithm for the csc model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8208–8217 (2019)

  33. Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp. 2528–2535 (2010)

  34. Heide, F., Heidrich, W., Wetzstein, G.: Fast and flexible convolutional sparse coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5135–5143 (2015)

  35. He, J., Yu, L., Liu, Z., Yang, W.: Image super-resolution by learning weighted convolutional sparse coding. Signal Image Video Process. 1–9 (2021)

  36. Gu, S., Zuo, W., Xie, Q., Meng, D., Feng, X., Zhang, L.: Convolutional sparse coding for image super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1823–1831 (2015)

  37. Papyan, V., Romano, Y., Sulam, J., Elad, M.: Convolutional dictionary learning via local processing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5296–5304 (2017)

  38. Zhu, Y., Lucey, S.: Convolutional sparse coding for trajectory reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 529–540 (2013)

    Article  Google Scholar 

  39. Liu, Y., Chen, X., Ward, R.K., Wang, Z.J.: Image fusion with convolutional sparse representation. IEEE Signal Process. Lett. 23(12), 1882–1886 (2016)

    Article  Google Scholar 

  40. Wang, L., Dou, J., Qin, P., Lin, S., Gao, Y., Wang, R., Zhang, J.: Multimodal medical image fusion based on nonsubsampled Shearlet transform and convolutional sparse representation. Multimed. Tools Appl. 1–21 (2021)

  41. Chen, B., Li, J., Ma, B., Wei, G.: Convolutional sparse coding classification model for image classification. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 1918–1922 (2016)

  42. Liao, H.-W., Su, L.: Monaural source separation using Ramanujan subspace dictionaries. IEEE Signal Process. Lett. 25(8), 1156–1160 (2018)

    Article  Google Scholar 

  43. Grosse, R., Raina, R., Kwong, H., Ng, A.Y.: Shift-invariance sparse coding for audio classification. arXiv preprint arXiv:1206.5241 (2012)

  44. Wohlberg, B.: Efficient convolutional sparse coding. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 7173–7177 (2014)

  45. Bristow, H., Eriksson, A., Lucey, S.: Fast convolutional sparse coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 391–398 (2013)

  46. Chen, S., Billings, S.A., Luo, W.: Orthogonal least squares methods and their application to non-linear system identification. Int. J. Control 50(5), 1873–1896 (1989)

    Article  MATH  Google Scholar 

  47. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  48. Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: 2011 International Conference on Computer Vision. IEEE, pp. 471–478 (2011)

  49. Jiang, Z., Lin, Z., Davis, L.S.: Label consistent k-svd: Learning a discriminative dictionary for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2651–2664 (2013)

    Article  Google Scholar 

  50. Vu, T.H., Monga, V.: Fast low-rank shared dictionary learning for image classification. IEEE Trans. Image Process. 26(11), 5160–5175 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  51. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  52. Martinez, A., Benavente, R.: The ar face database: Cvc technical report, 24 (1998)

  53. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 1994 IEEE Workshop on Applications of Computer Vision. IEEE, pp. 138–142 (1994)

  54. Goel, N., Bebis, G., Nefian, A.: Face recognition experiments with random projection. In: Biometric Technology for Human Identification II, vol. 5779, pp. 426–437 (2005). International Society for Optics and Photonics

  55. Yuksel, A., Akarun, L., Sankur, B.: Hand vein biometry based on geometry and appearance methods. IET Comput. Vis. 5(6), 398–406 (2011)

    Article  MathSciNet  Google Scholar 

  56. Pour, A.N., Eslami, E., Haddadnia, J.: A new method for automatic extraction of region of interest from infrared images of dorsal hand vein pattern based on floating selection model. Int. J. Appl. Pattern Recognit. 2(2), 111–127 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical/Computer Engineering, Semnan University, Semnan, 3513119111, Semnan, Iran

    Ali Nozaripour

  2. Faculty of Electrical/Computer Engineering, Semnan University, Semnan, 3513119111, Semnan, Iran

    Hadi Soltanizadeh

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  1. Ali Nozaripour
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  2. Hadi Soltanizadeh
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Correspondence to Hadi Soltanizadeh.

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Nozaripour, A., Soltanizadeh, H. Image classification via convolutional sparse coding. Vis Comput 39, 1731–1744 (2023). https://doi.org/10.1007/s00371-022-02441-1

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