Advertisement

RGB Color Distribution Analysis Using Volumetric Fractal Dimension

  • Dalcimar Casanova
  • Odemir Martinez Bruno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

Over the years many approaches for texture analysis have been proposed. Most of these methods use, directly or indirectly, the spatial information to build the features. Although the spatial distribution of gray levels is a property a priori of the texture, some methods do not use this propriety to characterize it. The problem is that this class of methods has, generally, worst results than first one. Thus, in this work we propose a new method to classify color textures that does not use any type of spatial distribution information and still achieves high classification rates, comparable, if not better, than traditional texture analysis methods. The method is based on analysis of RGB color distribution using volumetric fractal dimension.

Keywords

Fractal Dimension Linear Discriminant Analysis Color Texture Pattern Recognition Letter Histogram Ratio 
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.

References

  1. 1.
    Backes, A.R., Casanova, D., Bruno, O.M.: Plant leaf identification based on volumetric fractal dimension. International Journal of Pattern Recognition and Artificial Intelligence 23(6), 1145–1160 (2009)CrossRefGoogle Scholar
  2. 2.
    Chellappa, R., Chatterjee, S.: Classification of textures using gaussian garkov random fields. IEEE Transactions on Acoustics, Speech, and Signal Processing 33(4), 959–963 (1985)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Fabbri, R., da F. Costa, L., Torelli, J.C., Bruno, O.M.: 2D euclidean distance transform algorithms: A comparative survey. ACM Computing Surveys 40(1), 1–44 (2008)CrossRefGoogle Scholar
  4. 4.
    Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  5. 5.
    Hirata, T.: A unified linear-time algorithm for computing distance maps. Information Processing Letters 58, 129–133 (1996)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 24(12), 1167–1186 (1991)CrossRefGoogle Scholar
  7. 7.
    Keller, J.M., Chen, S., Crownover, R.M.: Texture description and segmentation through fractal geometry. Computer Vision, Graphics, and Image Processing 45(2), 150–166 (1989)CrossRefGoogle Scholar
  8. 8.
    Manjunath, B.S., Ma, W.-Y.: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 837–842 (1996)CrossRefGoogle Scholar
  9. 9.
    Meijster, A., Roerdink, J.B.T.M., Hesselink, W.H.: A general algorithm for computing distance transforms in linear time. In: Proceedings of the 5th International Conference on Mathematical Morphology and its Applications to Image and Signal Processing, pp. 331–340 (2000)Google Scholar
  10. 10.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: Proceedings 16th International Conference on Pattern Recognition, pp. 701–706 (2002)Google Scholar
  11. 11.
    Paschos, G.: Fast color texture recognition using chromaticity moments. Pattern Recognition Letters 21(9), 837–841 (2000)CrossRefGoogle Scholar
  12. 12.
    Paschos, G., Petrou, M.: Histogram ratio features for color texture classification. Pattern Recognition Letters 24(1), 309–314 (2003)CrossRefGoogle Scholar
  13. 13.
    Randen, T., Husøy, J.H.: Filtering for texture classification: A comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(4), 291–310 (1999)CrossRefGoogle Scholar
  14. 14.
    Tricot, C.: Curves and Fractal Dimension. Springer (1995)Google Scholar
  15. 15.
    Tuceryan, M., Jain, A.K.: Texture segmentation using voronoi polygons. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(2), 211–216 (1990)CrossRefGoogle Scholar
  16. 16.
    Tuceryan, M., Jain, A.K.: Texture analysis. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) The Handbook of Pattern Recognition and Computer Vision, pp. 207–248. World Scientific (1998)Google Scholar
  17. 17.
  18. 18.
    Weszka, J.S., Dyer, C.R., Rosenfeld, A.: A comparative study of texture measures for terrain classification. IEEE Transactions on Systems, Man, and Cybernetics 6(4), 269–285 (1976)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dalcimar Casanova
    • 1
  • Odemir Martinez Bruno
    • 1
  1. 1.IFSC - Instituto de Física de São CarlosUSP - Universidade de São PauloSão CarlosBrasil

Personalised recommendations