Improvement of Statistical and Fractal Features for Texture Classification

  • Dan Popescu
  • Radu Dobrescu
  • Nicoleta Angelescu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 187)


Texture classification and segmentation have been studied using various approaches. The mean Grey-Level Co-occurrence Matrix, introduced by the authors, gives statistical features relatively insensitive to rotation and translation. On the other hand, texture analysis based on fractals is an approach that correlates texture coarseness and fractal dimension. By combining the two types of features, the discrimination power increases. The paper introduces the notion of effective fractal dimension which is an adapting fractal dimension to classification of texture and is calculated by elimination of a constant zone which appears in all textured images. In the case of colour images, we proposed a classification method based on minimum distance between the vectors of the effective fractal dimension of the fundamental colour components. The experimental results to classify real land textured images validate that effective fractal dimension offers a grater discrimination of classes than typical fractal distance based on complete box counting algorithm.


texture fractal dimension box-counting algorithm statistical features image processing texture classification 


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  1. 1.
    Tuceryan, M., Jain, A.: Texture Analysis. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) The Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 207–248. World Scientific Publishing Co. (1998)Google Scholar
  2. 2.
    Pesaresi, M.: Texture Analysis for Urban Pattern Recognition Using Fine-resolution Panchromatic Satellite Imagery. Geographical and Environmental Modelling 4(1), 43–63 (2000)CrossRefGoogle Scholar
  3. 3.
    Olujic, M., Milosevic, N., Oros, A., Jelinek, H.: Aggressive Posterior Retinopathy of Prematurity: Fractal Analysis of Images before and after Laser Surgery. In: Proc. of 18th Int. Conf. on Control Systems and Computer Science, pp. 877–882. Politehnica Press, Bucharest (2011)Google Scholar
  4. 4.
    Shapiro, L., Stockman, G.: Computer Vision. Prentice Hall (2001)Google Scholar
  5. 5.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture Features for Image Classification. IEEE Transactions on Systems, Man. and Cybernetics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  6. 6.
    Pentland, A.P.: Fractal based description of natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 661–674 (1984)CrossRefGoogle Scholar
  7. 7.
    Keller, J.M., Chen, S., Crowner, R.M.: Texture Description and Segmentation through Fractal Geometry. Computer Vision, Graphics and Image Processing 45, 150–166 (1989)CrossRefGoogle Scholar
  8. 8.
    Peitgen, H.O., Jurgens, H., Saupe, D.: Chaos and Fractals: New Frontiers of Science. Springer, New York (1992)Google Scholar
  9. 9.
    Kaplan, L.M.: Extended fractal analysis for texture classification and segmentation. IEEE Transactions on Image Processing 8(11), 1572–1585 (1999)CrossRefGoogle Scholar
  10. 10.
    Carbone, A.: Algorithm to estimate the Hurst exponent of high-dimensional fractals. Physical Review E 76 056703/1 - 056703/7 (2007) Google Scholar
  11. 11.
    Li, J., Du, Q., Sun, C.: An improved box-counting method for image fractal dimension estimation. Pattern Recognition 42(11), 2460–2469 (2009)zbMATHCrossRefGoogle Scholar
  12. 12.
    Zhang, J., Tan, T.: Brief review of invariant texture analysis methods. Pattern Recognition 35, 735–747 (2002)zbMATHCrossRefGoogle Scholar
  13. 13.
    Dobrescu, R., Popescu, D.: Image processing applications based on texture and fractal analysis. In: Qahwaji, R., Green, R., Hines, E. (eds.) Applied Signal and Image Processing: Multidisciplinary Advancements, pp. 226–250. IGI Global Publishing (2011)Google Scholar
  14. 14.
    Popescu, D., Dobrescu, R.: Carriage road pursuit based on statistical and fractal analysis of the texture. International Journal of Education and Information Technologies 2(11), 62–70 (2008)Google Scholar
  15. 15.
    Wu, C.M., Chen, Y.C., Hsieh, K.S.: Texture features for classification of ultra-sonic liver images. IEEE Transactions on Medical Imaging 11, 141–152 (1992)CrossRefGoogle Scholar
  16. 16.
    Mandelbrot, B.B.: Fractals: Form, Chance and Dimension. W.H. Freeman and Company, San Francisco (1977)zbMATHGoogle Scholar
  17. 17.
    Popescu, D., Dobrescu, R., Angelescu, N.: Fractal Analysis of Textures Based on Modified Box-Counting Algorithm. In: Proc. of 18th Int. Conf. on Control Systems and Computer Science, pp. 894–898. Ed. Politehnica Press, Bucharest (2011)Google Scholar
  18. 18.
    Popescu, D., Dobrescu, R., Angelescu, N.: Colour textures discrimination of land images by fractal techniques. In: Proc. 4th Int. Conf. REMOTE 2008, Venice, pp. 51–56 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dan Popescu
    • 1
  • Radu Dobrescu
    • 1
  • Nicoleta Angelescu
    • 2
  1. 1.Faculty of Automatic Control and ComputersPOLITEHNICA University of BucharestBucharestRomania
  2. 2.Faculty of Electrical EngineeringValahia University of TargovisteTargovisteRomania

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