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Wavelet Domain Features for Texture/Pattern Description, Classification and Replicability Analysis

  • Laurent Balmelli
  • Aleksandra Mojsilović
Part of the Computational Imaging and Vision book series (CIVI, volume 19)

Abstract

We present a new wavelet domain technique for texture/pattern analysis and test of replicability. The main property of the proposed features is that they measure pattern quality along the most important perceptual dimensions. In other words, we quantify and classify patterns according to their directionality, symmetry, regularity and type of regularity. After the feature extraction, pattern classification (i.e. replicability analysis) is performed by traversing a tree. The algorithm is tested on a database with 340 images demonstrating an excellent classification accuracy. Additionally, we demonstrate the efficiency of our perceptual feature set with an application in texture/pattern retrieval.

Keywords

Wavelet Coefficient Color Pattern Wavelet Packet Texture Classification Markov Random Field 
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.

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References

  1. Amadsun, A. and King, R. (1989). Textural features corresponding to texture properties. SMC, 19:1264–1274.Google Scholar
  2. Bovik, A. C. (1991). Analysis of multichannel narrow-band filters for image texture segmentation. IEEE Trans. Signal Processing, 39(9):2025–2043.CrossRefGoogle Scholar
  3. Brodatz, P. (1966) . Textures: A photographic album for artists and designers. New York: Dover. Google Scholar
  4. Chang, T. and Kuo, C. (1993). Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Processing, 2:429–441.CrossRefGoogle Scholar
  5. Chellappa, R. and Chatterjee, S. (1985). Classification of textures using gaussian markov random fields. IEEE Trans. Acoust., Speech, Signal Processing, 33:959–963.MathSciNetCrossRefGoogle Scholar
  6. Do, M. N. and Vetterli, M. (2000). Texture similarity measurement using kullback-leibler distance on wavelet subbands. Proc. of IEEE International Conference on Image Processing.Google Scholar
  7. Dunn, D. and Higgins, W. E. (1995). Optimal gabor filters for texture segmentation. IEEE Trans. Image Processing, 4:947–964.CrossRefGoogle Scholar
  8. Haralick, R. M. (1979). Statistical and structural approaches to texture. Proc. of the IEEE, 67(5):786–804.CrossRefGoogle Scholar
  9. Healey, C. and Enns, J. T. (1998). Building perceptual textures to visualize multidimensional datasets. Proc. Visualization.Google Scholar
  10. Juleesz, B. (1965). Texture and visual perception. Scientific American, 212:38–54.CrossRefGoogle Scholar
  11. Juleesz, B. and Bergen, J. R. (1983). Textons: The fundamental elements in preattentive vision and perception of textures. Bell System Technical Journal, 62:1619–1645.Google Scholar
  12. Juleesz, B., Gilbert, E. N., Shepp, L. A., and Frish, H. L. (1973). Inability of humans to discriminate between visual textures that agree in second-order statistics — revisited. Perception, 2:391–405.CrossRefGoogle Scholar
  13. Liu, F. and Pickard, R. (1996). Eriodicity, directionality, and randomness: world features for image modeling and retrieval. IEEE Trans on Pattern Analysis and Machine Inteligence, 18(7):722–733.CrossRefGoogle Scholar
  14. Mallat, S. (1998). A Wavelet Tour of Signal Processing. Academic Press.zbMATHGoogle Scholar
  15. Mao, J. C. and Jain, J. K. (1992). Texture classification and segmentation usin multiresolution simultaneous autoregressive models. Pattern Recognition, 25(2):173–188.CrossRefGoogle Scholar
  16. Mojsilovic, A., Kovacevic, J., Hu, J., Safranek, R. J., and Ganapathy, K. (2000a). Matching and retrieval based on the vocabulary and grammar of color patterns. IEEE Trans. on Image Processing, 9(1) :38–54.CrossRefGoogle Scholar
  17. Mojsilovic, A., Kovacevic, J., Kall, D., Safranek, R. J., and Ganapathy, K. (2000b) . Vocabulary and grammar of color patterns. IEEE Trans. on Image Processing, 9(3):417–431.CrossRefGoogle Scholar
  18. Rao, A. R. and Lohse, G. L. (1996). Towards a texture naming system: Identifying relevant dimensions of texture. Vision Res., 36 (11) :1649–1669.CrossRefGoogle Scholar
  19. Tamura, H., Mori, S., and Yaawaki, T. (1982) . Textural features corresponding to visual perception. IEEE Transactions Systems, Man and Cybernetics, 8:460–473.CrossRefGoogle Scholar
  20. Unser, M. (1995). Texture classification and segmentation using wavelet frames. IEEE Trans. Image Processing, 4(11):1549–1560.CrossRefGoogle Scholar
  21. Vetterli, M. and Kovačević, J. (1995). Wavelets and subband coding. Prentice Hall PTR, Englewood Cliffs, New Jersey 07632.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Laurent Balmelli
    • 1
  • Aleksandra Mojsilović
    • 1
  1. 1.IBM Research DivisionT.J. Watson CenterUSA

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