Advertisement

Two-Dimensional Partial Least Squares and Its Application in Image Recognition

  • Mao-Long Yang
  • Quan-Sen Sun
  • De-Shen Xia
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)

Abstract

The problem of extracting optimal discriminant features is a critical step in image recognition. The algorithms such as classical iterative partial least squares (NIPALS and CPLS), non-iterative partial least squares based on orthogonal constraints (NIPLS), and partial least squares based on conjugation orthogonal constraints (COPLS) are introduced briefly. NIPLS and COPLS methods based on original image matrices are discussed where image covariance matrix is constructed directly using the original image matrices just like 2DPCA and 2DCCA. We call them 2DNIPLS and 2DCOPLS in the paper. Two arbitrary optimal discriminant features can be extracted by 2DCOPLS based on uncorrelated score constraints in theory. At the same time, it is pointed out that 2DCOPLS algorithm is more complicated than other PLS based algorithms. The results of experiments on ORL face database, Yale face database, and partial FERET face sub-database show that the 2DPLS algorithms presented are efficient and robust.

Keywords

Partial Least Squares (PLS) Uncorrelated Constraints 2DPCA Optimal Projection Image Recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wold, H.: Estimation of Principal Components and Related Models by Iterative Least Squares. In: Multivariate Analysis. Academic, New York (1966)Google Scholar
  2. 2.
    Wold, S., Sjölström, M., Erikson, L.: PLS_Regression: A Basic Tool of Chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109–130 (2001)CrossRefGoogle Scholar
  3. 3.
    Barker, M., Rayens, W.: Partial Least Squares for Discrimination. Journal of Chemometrics 17, 166–173 (2003)CrossRefGoogle Scholar
  4. 4.
    Wold, H.: Path with Latent Variables: The NIPALS Approach. In: Balock, H.M. (ed.) Quantitative Sociology: International Perspectives on Mathematical and Statistical Model Building, pp. 307–357. Academic Press, London (1975)Google Scholar
  5. 5.
    Höskuldsson, A.: PLS Regression Methods. Journal of Chemometrics 2, 211–228 (1988)CrossRefGoogle Scholar
  6. 6.
    Liu, Y.-S., Rayens, W.: PLS and Dimension Reduction for Classification. Computational Statistics 22, 189–208 (2007)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Yang, J., Yang, J.-Y., Jin, Z.: A Feature Extraction Approach Using Optimal Discriminant Transform and Image Recognition. Journal of Computer Research & Development 38, 1331–1336 (2001)Google Scholar
  8. 8.
    Frank, I.E., Friedman, H.: A Statistical View of Some Chemometrics Regression Tools. Technometrics 35, 109–135 (1993)zbMATHCrossRefGoogle Scholar
  9. 9.
    Han, L.: Kernel Partial Least Squares for Scientific Data Mining. PHD thesis, Rensselaer Polytechnic Institute, Troy, New York (2007)Google Scholar
  10. 10.
    Arenas-García, J., Petersen, K.B., Hansen, L.K.: Sparse Kernel Orthonormalized PLS for Feature Extraction in Large Data Sets. In: Advances in Neural Information Processing Systems, vol. 19. MIT Press, Cambridge (2007)Google Scholar
  11. 11.
    Baek, J.-S., Kim, M.: Face Recognition Using Partial Least Squares Components. Pattern Recognition 37, 1303–1306 (2004)zbMATHCrossRefGoogle Scholar
  12. 12.
    Jacob, A.: A Survey of Partial Least Squares Methods, with Emphasis on The Two-block Case. Technical Report, Department of Statistics, University of Washington, Seattle (2000)Google Scholar
  13. 13.
    Trygg, J., Wold, S.: Orthogonal Projections to Latent Structures. Journal of Chemometrics 16, 119–128 (2002)CrossRefGoogle Scholar
  14. 14.
    Yang, J., Zhang, D., Alejandro, F., Yang, J.-Y.: Two-dimensional PCA: A New Approach to Appearance-based Face Representation and Recognition. IEEE transactions on pattern analysis and machine intelligence 26, 131–137 (2004)CrossRefGoogle Scholar
  15. 15.
    Lee, S.-H., Choi, S.: Two-Dimensional Canonical Correlation Analysis. IEEE Signal Processing Letters 14, 735–738 (2007)CrossRefGoogle Scholar
  16. 16.
    Bolme, D.S., Beveridge, J. R., Teixeira, M., Draper, B. A.: The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure. In: Proceedings of 3rd International Conference on Computer Vision Systems (ICVS), pp.304–313 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mao-Long Yang
    • 1
    • 2
  • Quan-Sen Sun
    • 1
  • De-Shen Xia
    • 1
  1. 1.Institute of Computer ScienceNanjing University of Science & TechnologyNanjingChina
  2. 2.International Studies UniversityNanjingChina

Personalised recommendations