Texture Classification Based on Coevolution Approach in Multiwavelet Feature Space

  • Jing-Wein Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


To test the effectiveness of multiwavelets in texture classification with respect to scalar Daubechies wavelets, we study the evolutionary-based algorithm to evaluate the classification performance of each subset of selected feature. The approach creates two populations that have interdependent evolutions corresponding to inter and intra distance measure, respectively. With the proposed fitness function composed of the individuals in competition, the evolution of the distinct populations is performed simultaneously through a coevolutionary process and selects frequency channel features of greater discriminatory power. Consistently better performance of the experiments suggests that the multiwavelet transform features may contain more texture information for classification than the scalar wavelet transform features. Classification performance comparisons using a set of twelve Brodatz textured images and wavelet packet decompositions with the novel packet-tree feature selection support this conclusion.


  1. 1.
    Strela, V., Heller, N., Strang, G., Topiwala, P., and Heil, C.: The Application of Multiwavelet Filter Banks to Image Processing. IEEE Trans. Image Process., 8 (1999) 548–563CrossRefGoogle Scholar
  2. 2.
    Xia, X. G., Geronimo, J. S., Hardin, D. P., and Suter, B. W.: Design of Prefilters for Discrete Multiwavelet Transforms. IEEE Trans. Signal Process., 44 (1996) 25–35CrossRefGoogle Scholar
  3. 3.
    Daubechies, I. (ed.): Ten Lectures on Wavelets. SLAM, Philadelphia, Penn. (1992)Google Scholar
  4. 4.
    Siedlecki, W. and Sklansky, J.: A Note on Genetic Algorithm for Large-Scale Feature Selection. Pattern Recognition Letters, 10 (1989) 335–347zbMATHCrossRefGoogle Scholar
  5. 5.
    Wang, J. W., Chen, C. H., Chien, W. M., and Tsai, C. M.: Texture Classification using Non-Separable Two-Dimensional Wavelets. Pattern Recognition Letters, 19 (1998) 1225–1234zbMATHCrossRefGoogle Scholar
  6. 6.
    Goldberg, D. E. (ed.): Genetic Algorithms in Search, Optimization, and Machine Learning. MA: Addison-Wesley (1989)zbMATHGoogle Scholar
  7. 7.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, New York (1996)zbMATHGoogle Scholar
  8. 8.
    Brodatz, P. (ed.): Textures: A Photographic Album for Artists and Designers. NY: Dover (1966)Google Scholar
  9. 9.
    Devijver, P. A. and Kittler, J. (ed.): Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs, NJ (1982)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jing-Wein Wang
    • 1
  1. 1.Center of General StudiesNational Kaohsiung University of Applied SciencesKaohsiungTaiwan, R.O.C.

Personalised recommendations