A Graph-Based Algorithm for Supervised Image Classification

  • Ke Du
  • Jinlong Liu
  • Xingrui Zhang
  • Jianying Feng
  • Yudong Guan
  • Stéphane Domas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)


Manifold learning is a main stream research track used for dimensionality reduction as a method to select features. Many variants have been proposed with good performance. A novel graph-based algorithm for supervised image classification is introduced in this paper. It makes the use of graph embedding to increase the recognition accuracy. The proposed algorithm is tested on four benchmark datasets of different types including scene, face and object. The experimental results show the validity of our solution by comparing it with several other tested algorithms.


Graph-based Supervised learning Image classification 


  1. 1.
    Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: 20th International Conference on Machine Learning, Washington DC, USA, pp. 912–919 (2003)Google Scholar
  2. 2.
    He, X., Niyogi, P.: Locality preserving projections. Adv. Neural Inf. Proc. Syst. 2(5), 153–160 (2004)Google Scholar
  3. 3.
    Cheng, H., Liu, Z., Yang, J.: Sparsity induced similarity measure for label propagation. In: 12th IEEE International Conference on Computer Vision (ICCV), pp. 317–324. IEEE, Kyoto (2009)Google Scholar
  4. 4.
    Pei, X., Chen, C., Guan, Y.: Joint sparse representation and embedding propagation learning: a framework for graph-based semisupervised learning. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 2949–2960 (2017)MathSciNetCrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Ni, B., Yan, S., Kassim, A.: Learning a propagable graph for semisupervised learning: classification and regression. IEEE Trans. Knowl. Data Eng. 24(1), 114–126 (2012)CrossRefGoogle Scholar
  7. 7.
    Nie, F., Xu, D., Li, X., Xiang, S.: Semisupervised dimensionality reduction and classification through virtual label regression. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(3), 675–685 (2011)CrossRefGoogle Scholar
  8. 8.
    He, X., Cai, D., Han, J.: Semi-supervised discriminant analysis. In: 11th IEEE International Conference on Computer Vision (ICCV), pp. 1–7. IEEE, Rio de Janeiro (2007)Google Scholar
  9. 9.
    Yan, S., Xu, D., Yang, Q., Zhang, L., Tang, X., Zhang, H.J.: Discriminant analysis with tensor representation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 526–532. IEEE, San Diego (2005)Google Scholar
  10. 10.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)CrossRefGoogle Scholar
  11. 11.
    Brand, M.: Continuous nonlinear dimensionality reduction by kernel eigenmaps. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 547–554. ACM, Acapulco (2010)Google Scholar
  12. 12.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  13. 13.
    Tenenbaum, J.B., De, S.V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  14. 14.
    Yuan, Y., Mou, L., Lu, X.: Scene recognition by manifold regularized deep learning architecture. IEEE Trans. Neural Netw. Learn. Syst. 26(10), 2222–2233 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kong, D., Ding, C.H.Q., Huang, H., Nie, F.: An iterative locally linear embedding algorithm. In: 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, UK (2010)Google Scholar
  16. 16.
    Hou, C., Nie, F., Li, X., Yi, D., Wu, Y.: Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans. Cybern. 44(6), 793–804 (2014)CrossRefGoogle Scholar
  17. 17.
    Li, L.J., Li, F.F.: What, where and who? Classifying events by scene and object recognition. In: 11th IEEE International Conference on Computer Vision (ICCV), pp. 1–8. IEEE, Rio de Janeiro (2007)Google Scholar
  18. 18.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE, New York (2006)Google Scholar
  19. 19.
    Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: 2ed IEEE Workshop on Applications of Computer Vision, pp. 138–142. IEEE, Sarasota (2010)Google Scholar
  20. 20.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (coil-20). Technical report CUCS-005-96, Location (1996)Google Scholar
  21. 21.
    Raducanu, B., Dornaika, F.: A supervised non-linear dimensionality reduction approach for manifold learning. Pattern Recogn. 45(6), 2432–2444 (2012)CrossRefGoogle Scholar
  22. 22.
    Yu, G., Zhang, G., Domeniconi, C., Yu, Z., You, J.: Semi-supervised classification based on random subspace dimensionality reduction. Pattern Recogn. 45(3), 1119–1135 (2012)CrossRefGoogle Scholar
  23. 23.
    Geng, X., Zhan, D.C., Zhou, Z.H.: Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 35(6), 1098–1107 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ke Du
    • 1
  • Jinlong Liu
    • 2
  • Xingrui Zhang
    • 2
  • Jianying Feng
    • 2
  • Yudong Guan
    • 2
  • Stéphane Domas
    • 1
  1. 1.FEMTO-ST Institute, UMR 6174 CNRS, University of Bourgogne Franche-ComtéBelfortFrance
  2. 2.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina

Personalised recommendations