Enhancing Gabor Wavelets Using Volumetric Fractal Dimension

  • Alvaro Gomez Zuniga
  • Odemir Martinez Bruno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


Texture plays an important role on image analysis and computer vision. Local spatial variations of intensity and color indicate significant differences among several types of surfaces. One of the most widely adopted algorithms for texture analysis is the Gabor wavelets. This technique provides a multi-scale and multi-orientation representation of an image which is capable of characterizing different patterns of texture effectively. However, the texture descriptors used does not take full advantage of the richness of detail from the Gabor images generated in this process. In this paper, we propose a new method for extracting features of the Gabor wavelets space using volumetric fractal dimension. The results obtained in experimentation demonstrate that this method outperforms earlier proposed methods for Gabor space feature extraction and creates a more accurate and reliable method for texture analysis and classification.


Volumetric fractal dimension Texture analysis Gabor Wavelets Feature extraction 


  1. 1.
    Rosenfeld, A., Lipkin, B.S.: Picture Processing and Psychopictorics. Academic Press, London (1970)Google Scholar
  2. 2.
    Daugman, J.G.: Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research 20, 847–856 (1980)CrossRefGoogle Scholar
  3. 3.
    Manjunath, B.S., Ma, W.-Y.: Texture features for browsing and retrieval of image data. IEEE PAMI, 837–842 (1996)Google Scholar
  4. 4.
    Andrysiak, T., Choras, M.: Image Retrieval Based on Hierarchical Gabor Filters. Intl J. Applied Mathematics and Computer Science 15(4), 471–480 (2005)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Bandzi, P., Oravec, M., Pavlovicova, J.: New Statistics for Texture Classification Based on Gabor Filters. Intl. Radioengineering J. 16(3), 133–137 (2007)Google Scholar
  6. 6.
    Clausi, D.A., Deng, H.: Fusion of Gabor filter and co-occurrence probability features for texture recognition. IEEE Transactions on Image Processing 14(7), 925–936 (2005)CrossRefGoogle Scholar
  7. 7.
    Shahabi, F., Rahmati, M.: Comparison of gabor-based features for writer identification of farsi/arabic handwriting. In: Proc. of 10th Intl. Workshop on Frontiers in Handwriting Recognition, pp. 545–550 (2006)Google Scholar
  8. 8.
    Muneeswaran, K., Ganesan, I., Arumugam, S., Harinarayan, P.: A novel approach combining gabor wavelet and moments for texture segmentation. Intl. J. of Wavelets, Multiresolution and Information Processing 3(4), 559–572 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Qaiser, N., Hussain, M., Hussain, A., Qaiser, N.: Texture Recognition by Fusion of Optimized Moment Based and Gabor Energy Features. Intl. J. CSNS 8(2), 264–270 (2008)Google Scholar
  10. 10.
    Andrysiak, T., Choras, M.: Image Retrieval Based on Hierarchical Gabor Filters. Intl. J. Applied Mathematics and Computer Science 15(4), 471–480 (2005)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Grigorescu, S.E., Petkov, N., Kruizinga, P.: Comparison of texture Features based on Gabor Filters. IEEE Transactions on Image processing 11(10), 1160–1167 (2002)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition. In: Tenth IEEE ICCV, vol. 1, pp. 786–791 (2005)Google Scholar
  13. 13.
    Lei, Z., Liao, S., He, R., Pietikäinen, M., Li, S.: Gabor volume based local binary pattern for face representation and recognition. In: 8th IEEE Intl. Conference on Automatic Face and Gesture Recognition, pp. 1–6 (2008)Google Scholar
  14. 14.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  15. 15.
    Casanova, D., de Mesquita Sá Jr., J.J., Bruno, O.M.: Plant leaf identification using Gabor wavelets. Intl. J. of Imaging Systems and Technology 19(3), 236–243 (2009)CrossRefGoogle Scholar
  16. 16.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Sys. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  17. 17.
    Qaiser, N., Hussain, M.: Optimum Window-size Computation for Moment Based Texture Segmentation. In: Proc. IEEE INMIC, pp. 25–29 (2003)Google Scholar
  18. 18.
    Mandelbrot, B.B.: The Fractal Geometry of Nature, W.H. (1982)Google Scholar
  19. 19.
    Backes, A.R., Bruno, O.M.: Fractal and Multi-Scale Fractal Dimension analysis: a comparative study of Bouligand-Minkowski method. INFOCOMP (UFLA) 7, 74–83 (2008)Google Scholar
  20. 20.
    Backes, A.R., Casanova, D., Bruno, O.M.: Plant leaf identification based on volumetric fractal dimension. IEEE PAMI 23, 1145–1160 (2009)Google Scholar
  21. 21.
    Fabbri, R., Da Costa, F.L., Torelli, J.C., Bruno, O.M.: 2D Euclidean distance transform algorithms: A comparative survey. ACM Computing Surveys (CSUR) 40(1), 1–44 (2008)CrossRefGoogle Scholar
  22. 22.
    Brodatz, P.: Textures; a photographic album for artists and designers (1996)Google Scholar
  23. 23.
    Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14, 21–30 (2004)CrossRefGoogle Scholar
  24. 24.
    Daugman, J.: Gabor wavelets and statistical pattern recognition. In: The Handbook of Brain Theory and N.N., 2nd edn., pp. 457–463. MIT, Cambridge (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alvaro Gomez Zuniga
    • 1
  • Odemir Martinez Bruno
    • 2
  1. 1.Instituto de Ciências Matemáticas e de ComputaçãoBrazil
  2. 2.Instituto de Física de São CarlosUniversidade de São PauloSão CarlosBrasil

Personalised recommendations