Multi-label Feature Transform for Image Classifications

  • Hua Wang
  • Heng Huang
  • Chris Ding
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)


Image and video annotations are challenging but important tasks to understand digital multimedia contents in computer vision, which by nature is a multi-label multi-class classification problem because every image is usually associated with more than one semantic keyword. As a result, label assignments are no longer confined to class membership indications as in traditional single-label multi-class classification, which also convey important characteristic information to assess object similarity from knowledge perspective. Therefore, besides implicitly making use of label assignments to formulate label correlations as in many existing multi-label classification algorithms, we propose a novel Multi-Label Feature Transform (MLFT) approach to also explicitly use them as part of data features. Through two transformations on attributes and label assignments respectively, MLFT approach uses kernel to implicitly construct a label-augmented feature vector to integrate attributes and labels of a data set in a balanced manner, such that the data discriminability is enhanced because of taking advantage of the information from both data and label perspectives. Promising experimental results on four standard multi-label data sets from image annotation and other applications demonstrate the effectiveness of our approach.


Support Vector Machine Principle Component Analysis Image Annotation Pairwise Similarity Label Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  2. 2.
    Chen, G., Song, Y., Wang, F., Zhang, C.: Semi-supervised Multi-label Learning by Solving a Sylvester Equation. In: Jonker, W., Petković, M. (eds.) SDM 2008. LNCS, vol. 5159, Springer, Heidelberg (2008)Google Scholar
  3. 3.
    Chung, F.: Spectral graph theory. Amer. Mathematical Society, Providence (1997)zbMATHGoogle Scholar
  4. 4.
    Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Proc of NIPS (2001)Google Scholar
  5. 5.
    Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Griffiths, T., Ghahramani, Z.: Infinite latent feature models and the Indian buffet process. In: Proc. of NIPS (2006)Google Scholar
  7. 7.
    Hall, K.: An r-dimensional quadratic placement algorithm. Management Science, 219–229 (1970)Google Scholar
  8. 8.
    Ji, S., Tang, L., Yu, S., Ye, J.: Extracting shared subspace for multi-label classification. In: Proc of SIGKDD (2008)Google Scholar
  9. 9.
    Jolliffe, I.: Principal component analysis. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  10. 10.
    Kang, F., Jin, R., Sukthankar, R.: Correlated label propagation with application to multi-label learning. In: Proc of CVPR, pp. 1719–1726 (2006)Google Scholar
  11. 11.
    Lewis, D., Yang, Y., Rose, T., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)Google Scholar
  12. 12.
    Liu, Y., Jin, R., Yang, L.: Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: Proc. of AAAI, p. 421 (2006)Google Scholar
  13. 13.
    Naphade, M., Kennedy, L., Kender, J., Chang, S., Smith, J., Over, P., Hauptmann, A.: LSCOM-lite: A light scale concept ontology for multimedia understanding for TRECVID 2005. Tech. rep., Technical report, IBM Research Tech. Report, RC23612, W0505-104 (2005)Google Scholar
  14. 14.
    Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proc. of ACM Multimedia (2006)Google Scholar
  15. 15.
    Wang, H., Huang, H., Ding, C.: Image Annotation Using Multi-label Correlated Greens Function. In: Proc. of ICCV, pp. 2029–2034 (2009)Google Scholar
  16. 16.
    Yu, K., Yu, S., Tresp, V.: Multi-label informed latent semantic indexing. In: Proc of SIGIR (2005)Google Scholar
  17. 17.
    Zha, Z., Mei, T., Wang, J., Wang, Z., Hua, X.: Graph-based semi-supervised learning with multi-label. In: Proc. of IEEE ICME, pp. 1321–1324 (2008)Google Scholar
  18. 18.
    Zhang, Y., Zhou, Z.: Multi-Label Dimensionality Reduction via Dependence Maximization. In: Proc of AAAI, pp. 1503–1505 (2008)Google Scholar
  19. 19.
    Zhu, S., Ji, X., Xu, W., Gong, Y.: Multi-labelled classification using maximum entropy method. In: Proc. of SIGIR, pp. 274–281 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hua Wang
    • 1
  • Heng Huang
    • 1
  • Chris Ding
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

Personalised recommendations