An Incremental Structured Part Model for Image Classification

  • Huigang Zhang
  • Xiao Bai
  • Jian Cheng
  • Jun Zhou
  • Huijie Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


The state-of-the-art image classification methods usually require many training samples to achieve good performance. To tackle this problem, we present a novel incremental method in this paper, which learns a part model to classify objects using only a small number of training samples. Our model captures the inherent connections of the semantic parts of objects and builds structural relationship between them. In the incremental learning stage, we use high entropy images that have been accepted by users to update the learned model. The proposed approach is evaluated on two datasets, which demonstrates its advantages over several alternative classification methods in the literature.


Image classification semantic parts structural relationship incremental learning 


  1. 1.
    Fei-Fei, L., Fergus, R., Torralba, A.: Recognizing and learning object categories. In: ICCV Short Course (2005)Google Scholar
  2. 2.
    Yang, L., Jin, R., Sukthankar, R., Jurie, F.: Unifying discriminative visual codebook generation with classifier training for object category recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)Google Scholar
  3. 3.
    Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from google’s image search. In: IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1816–1823 (2005)Google Scholar
  4. 4.
    Bunke, H., Sanfeliu, A.: Syntactic and structural pattern recognition: theory and applications, vol. 7. World Scientific Pub. Co. Inc. (1990)Google Scholar
  5. 5.
    Xiao, B., Hancock, E., Wilson, R.: Graph characteristics from the heat kernel trace. Pattern Recognition 42(11), 2589–2606 (2009)zbMATHCrossRefGoogle Scholar
  6. 6.
    Wilson, R., Hancock, E., Luo, B.: Pattern vectors from algebraic graph theory. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(7), 1112–1124 (2005)CrossRefGoogle Scholar
  7. 7.
    Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. International Journal of Computer Vision 61(1), 55–79 (2005)CrossRefGoogle Scholar
  8. 8.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  9. 9.
    Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: IEEE International Conference on Computer Vision, ICCV 2005, pp. 1458–1465 (2005)Google Scholar
  10. 10.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794–1801 (2009)Google Scholar
  11. 11.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp. 3360–3367 (2010)Google Scholar
  12. 12.
    Li, L., Fei-Fei, L.: Optimol: Automatic online picture collection via incremental model learning. International Journal of Computer Vision 88(2), 147–168 (2010)CrossRefGoogle Scholar
  13. 13.
    Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1778–1785 (2009)Google Scholar
  14. 14.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62(1), 61–81 (2005)Google Scholar
  16. 16.
    Wyszecki, G., Stiles, W.: Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley, New York (1982)Google Scholar
  17. 17.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (6), 679–698 (1986)Google Scholar
  18. 18.
    Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers 10(3), 61–74 (1999)Google Scholar
  19. 19.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)Google Scholar
  20. 20.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes challenge 2007 (voc 2007) results (2007)Google Scholar
  21. 21.
    Su, Y., Allan, M., Jurie, F.: Improving object classification using semantic attributes. In: Proceedings of the British Machine Vision Conference, BMVC 2010, pp. 26–21 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huigang Zhang
    • 1
  • Xiao Bai
    • 1
  • Jian Cheng
    • 2
  • Jun Zhou
    • 3
  • Huijie Zhao
    • 1
  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Institute of Automation Chinese Academy of SciencesBeijingChina
  3. 3.School of Information and Communication TechnologyGriffith UniversityNathanAustralia

Personalised recommendations