Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration

  • Ludovic Journaux
  • Marie-France Destain
  • Johel Miteran
  • Alexis Piron
  • Frederic Cointault
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)


In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried out images from two different sources, the well known Brodatz database and our leaf texture images database.


Texture classification Motion Descriptors Dimensionality reduction 


  1. 1.
    Arivazhagan, S., Ganesan, L., Priyal, S.P.: Texture classification using Gabor wavelets based rotation invariant features. Pattern Recognition Letters 27, 1976–1982 (2006)CrossRefGoogle Scholar
  2. 2.
    Hughes, G.F.: On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory 14, 55–63 (1968)CrossRefGoogle Scholar
  3. 3.
    Aldo Lee, J., Archambeau, C., Verleysen, M.: Locally Linear Embedding versus Isotop. In: ESANN 2003 proceedings, Bruges (Belgium), pp. 527–534 (2003)Google Scholar
  4. 4.
    Aldo Lee, J., Lendasse, A., Verleysen, M.: Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis. Neurocomputing 57, 49–76 (2004)CrossRefGoogle Scholar
  5. 5.
    Journaux, L., Foucherot, I., Gouton, P.: Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods. Optical Engineering 45, 67002 (2006)CrossRefGoogle Scholar
  6. 6.
    Niskanen, M., Silven, O.: Comparison of dimensionality reduction methods for wood surface inspection. In: QCAV 2003 proceedings, Gatlinburg, Tennessee, USA, pp. 178–188 (2003)Google Scholar
  7. 7.
    Gauthier, J.-P., Bornard, G., Silbermann, M.: Harmonic analysis on motion groups and their homogeneous spaces. IEEE Transactions on Systems, Man and Cybernetics 21, 159–172 (1991)CrossRefMathSciNetzbMATHGoogle Scholar
  8. 8.
    Lemaître, C., Smach, F., Miteran, J., Gauthier, J.-P., Atri, M.: A comparative study of motion descriptors and Zernike moments in color object recognition. In: proceeding of International Multi-Conference on Systems, Signal and Devices. IEEE, Hammamet, Tunisia (2007)Google Scholar
  9. 9.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)Google Scholar
  10. 10.
    Valkealahti, K., Oja, E.: Reduced multidimensional cooccurrence histograms in texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 90–94 (1998)CrossRefGoogle Scholar
  11. 11.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. (2001)Google Scholar
  12. 12.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  13. 13.
    Vapnik, V.: Statistical learning theory. John Wiley & sons, inc., Chichester (1998)zbMATHGoogle Scholar
  14. 14.
    Schapire, R.E.: The strenght of weak learnability. Machine Learning 5, 197–227 (1990)Google Scholar
  15. 15.
    Miteran, J., Gorria, P., Robert, M.: Geometric classification by stress polytopes. Performances and integrations. Traitement du signal 11, 393–407 (1994)Google Scholar
  16. 16.
    Abe, S.: Support Vector Machines for Pattern Classification. Springer, Heidelberg (2005)Google Scholar
  17. 17.
    Rumelhart, D.E., McClelland, J.L., Group, a.t.P.R.: Parallel Distributed Processing, vol. 1. MIT Press, Cambridge (1986)Google Scholar
  18. 18.
    Aldo Lee, J., Verleysen, M.: Nonlinear Dimensionality Reduction. Springer, Heidelberg (2007)Google Scholar
  19. 19.
    Camastra, F., Vinciarelli, A.: Estimating the Intrinsic Dimension of Data with a Fractal-Based Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 1404–1407 (2002)CrossRefGoogle Scholar
  20. 20.
    Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A blind source separation technique using second order statistics. IEEE Transactions on signal processing 45, 434–444 (1997)CrossRefGoogle Scholar
  21. 21.
    Friedman, J.H., Tukey, J.W.: A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on computers C23, 881–890 (1974)CrossRefGoogle Scholar
  22. 22.
    HyvÄarinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10, 626–634 (1999)CrossRefGoogle Scholar
  23. 23.
    Sammon, J.W.: A nonlinear mapping for data analysis. IEEE Transactions on Computers C-18, 401–409 (1969)CrossRefGoogle Scholar
  24. 24.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  25. 25.
    Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Cambridge University Press, Cambridge (2004)Google Scholar
  26. 26.
    Ham, J., Lee, D.D., Mika, S., Schölkopf, B.: A kernel view of the dimensionality reduction of manifolds. In: 21th ICML 2004, Banff, Canada, pp. 369–376 (2004)Google Scholar
  27. 27.
    Schölkopf, B., Smola, A.J., Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  28. 28.
    Choi, H., Choi, S.: Robust kernel Isomap. Pattern Recognition 40, 853–862 (2007)CrossRefzbMATHGoogle Scholar
  29. 29.
    Schölkopf, B., Burges, J.C.C., Smola, A.J.: Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar
  30. 30.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  31. 31.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 1373–1396 (2003)CrossRefzbMATHGoogle Scholar
  32. 32.
    Demartines, P., Hérault, J.: Curvilinear Component Analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Transactions on neural networks 8, 148–154 (1997)CrossRefGoogle Scholar
  33. 33.
    Kittler, J.: Feature set search algorithms. In: Noordhoff, S. (ed.) Pattern Recognition and Signal Processing. Chen, H., pp. 41–60 (1978)Google Scholar
  34. 34.
    Fletcher, R.: Practical Methods of Optimization. John Wiley & Sons, Chichester (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ludovic Journaux
    • 1
  • Marie-France Destain
    • 1
  • Johel Miteran
    • 2
  • Alexis Piron
    • 1
  • Frederic Cointault
    • 3
  1. 1.Unité de Mécanique et ConstructionFaculté Universitaire des Sciences Agronomiques de GemblouxGembloux
  2. 2.Lab. Le2iUniversité de BourgogneDijon CedexFrance
  3. 3.ENESAD UP GAPDijon CedexFrance

Personalised recommendations