LPPNet: A Learning Network for Image Feature Extraction and Classification

  • Guodong Li
  • Haishun DuEmail author
  • Meihong Xiao
  • Sheng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


PCANet is a very simple learning network for image classification. Inspired by PCANet, we propose a new learning network, referred to as LPPNet, for image feature extraction and classification. Different from PCANet, LPPNet takes the class information and the local geometric structure of data into account simultaneously. In LPPNet, local preserving projections (LPP) is first employed to learn filters, and then binary hashing and block histograms are used for indexing and pooling. Experimental results on several image datasets verify the effectiveness and robustness of LPPNet for image feature extraction and classification.


LPPNet Learning network Feature extraction Image classification 



This work is supported in part by the NSFC-Henan Talent Jointly Training Foundation of China (no. U1504621) and the Key Scientific Research Project of University in Henan Province of China (no. 18A120001).


  1. 1.
    Jain, A.K., Duin, R.P.W., Miao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)CrossRefGoogle Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (2002)CrossRefGoogle Scholar
  4. 4.
    Tenenbaum, J.B., Silva, V.d., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)Google Scholar
  5. 5.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  6. 6.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)CrossRefGoogle Scholar
  7. 7.
    Lai, Z., Xu, Y., Yang, J., Shen, L., Zhang, D.: Rotational invariant dimensionality reduction algorithms. IEEE Trans. Cybern. 47(11), 3733–3746 (2017)CrossRefGoogle Scholar
  8. 8.
    Shi, X., Guo, Z., Lai, Z., Yang, Y., Bao, Z., Zhang, D.: A framework of joint graph embedding and sparse regression for dimensionality reduction. IEEE Trans. Image Process. 24(4), 1341–1355 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Wang, Q., Gao, Q., Xie, D., Gao, X., Wang, Y.: Robust dlpp with nogreedy \(\ell_1\) minimization and maximization. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 738–743 (2018)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1615–1618 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Guodong Li
    • 1
  • Haishun Du
    • 1
    Email author
  • Meihong Xiao
    • 1
  • Sheng Wang
    • 1
  1. 1.School of Computer Science and Information EngineeringHenan UniversityKaifengChina

Personalised recommendations