Classifying image texture with statistical landscape features

Theoretical Advances


This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.


Texture Texture classification Statistical landscape features Feature curve 



The research work presented in this paper is supported by National Natural Science Foundation of China, Grant No. 60275010, and Science and Technology Commission of Shanghai Municipality, Grant No. 04JC14014.


  1. 1.
    Coggins JM (1982) A framework for texture analysis based on spatial filtering. PhD thesis, Computer Science Department, Michigan State UniversityGoogle Scholar
  2. 2.
    Tamura H, Mori S, Yamawaki Y (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern SMC 8(6):460–473Google Scholar
  3. 3.
    Haralick RM (1979). Statistical and structural approaches to texture. Proc IEEE 67(5):786–804CrossRefGoogle Scholar
  4. 4.
    Karu K, Jain AK, Bolle RM (1996) Is there any texture in the image? Pattern Recognit 29(9):1437–1446CrossRefGoogle Scholar
  5. 5.
    Randen T, Husøy JH (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4):291–310CrossRefGoogle Scholar
  6. 6.
    Panjwani DK, Healey G (1995) Markov random field models for unsupervised segmentation of textured color images. IEEE Trans Pattern Anal Mach Intell 17(10):939–954CrossRefGoogle Scholar
  7. 7.
    Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842CrossRefGoogle Scholar
  8. 8.
    Clerc M, Mallat S (2002) The texture gradient equation for recovering shape from texture. IEEE Trans Pattern Anal Mach Intell 24(4):536–549CrossRefGoogle Scholar
  9. 9.
    Singh M, Singh S (2002) Spatial texture analysis: a comparative study. In: Proceedings of the 15th international conference on pattern recognition (ICPR’02), vol 1, pp 676–679Google Scholar
  10. 10.
    Tuceryan M, Jain AK (1993) Texture analysis. Handbook pattern recognition and computer vision, chap 2. In: Chen CH, Pau LF, Wang PSP (eds) World Scientific, Singapore, pp 235–276Google Scholar
  11. 11.
    Weszka JS, Dyer CR, Rosenfeld A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybern SMC 6(4):269–285MATHGoogle Scholar
  12. 12.
    Jones DJ, Jackway PT (2000) Granolds: a novel texture representation. Pattern Recognit 33(6):1033–1045CrossRefGoogle Scholar
  13. 13.
    Sivakumar K, Goutsias J (1999) Morphologically constrained GRFS: applications to texture synthesis and analysis. IEEE Trans Pattern Anal Mach Intell 21(2):99–113CrossRefGoogle Scholar
  14. 14.
    Rosenfeld A, Thurston M (1971) Edge and curve detection for visual scene analysis. IEEE Trans Comput C-20:562–569CrossRefGoogle Scholar
  15. 15.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3(6):610–621CrossRefGoogle Scholar
  16. 16.
    Conners RW, Harlow CA (1980) A theoretical comparison of texture algorithms. IEEE Trans Pattern Anal Mach Intell 2(3):204–222MATHGoogle Scholar
  17. 17.
    Murino V, Ottonello C, Pagnan S (1998) Noisy texture classification: a higher order statistics approach. Pattern Recognit 34(4):383–393CrossRefGoogle Scholar
  18. 18.
    Liu XW, Wang DL (2003) Texture classification using spectral histograms. IEEE Trans Image Process 12(6):661–670CrossRefGoogle Scholar
  19. 19.
    Kaplan LM (1999) Extended fractal analysis for texture classification and segmentation. IEEE Trans Image Process 8(11):1572–1585CrossRefGoogle Scholar
  20. 20.
    Krishnamachari S, Chellappa R (1997) Multiresolution gauss-markov random field models for texture segmentation. IEEE Trans Image Process 6(2):251–267CrossRefGoogle Scholar
  21. 21.
    Cross G, Jain A (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 5(1):25–39Google Scholar
  22. 22.
    Bennett J, Khotanzad A (1998) Modeling textured image using generalized long correlation models. IEEE Trans Pattern Anal Mach Intell 20(12):1365–1370CrossRefGoogle Scholar
  23. 23.
    Garcia P, Petrou M, Kamata S (1999) The use of Boolean model for texture analysis of grey images. Comput Vis Image Underst 74(3):227–235CrossRefGoogle Scholar
  24. 24.
    Laws KI (1980) Rapid texture identification. In: Proceedings of the SPIE conference image processing for missile guidance, pp 376–380Google Scholar
  25. 25.
    Jain AK, Farrokhnia F (1991) Unsupervised texture segmentation using Gabor filters. Pattern Recognit 24(12):1167–1186CrossRefGoogle Scholar
  26. 26.
    Azencott R, Wang JP, Younes L (1997) Texture classification using windowed fourier filters. IEEE Trans Pattern Anal Mach Intell 19(2):148–153CrossRefGoogle Scholar
  27. 27.
    Arivazhagan S, Ganesan L (2003) Texture classification using wavelet transform. Pattern Recognit Lett 24(9–10):1513–1521MATHCrossRefGoogle Scholar
  28. 28.
    Pun C-M, Lee M-C (2003) Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Anal Mach Intell 25(5):590–603CrossRefGoogle Scholar
  29. 29.
    Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4:1549–1560CrossRefGoogle Scholar
  30. 30.
    Mojsilovic A, Popovic MV, Rackov DM (2000) On the selection of an optimal wavelet basis for texture classification. IEEE Trans Image Process 9(12):2043–2050MATHCrossRefMathSciNetGoogle Scholar
  31. 31.
    Chen YQ, Nixon MS, Thomas DW (1995) Statistical geometrical features for texture classification. Pattern Recognit 28(4):537–552CrossRefGoogle Scholar
  32. 32.
    Brodatz P (1966) Textures: a photographic album for artists and designers. Dover, Paris.∼tranden/brodatz.html
  33. 33.
    Picard R (1995) Chris Graczyk, Steve Mann, Josh Wachman, Len Picard, and Lee Campbell. Vistex. via Copyright 1995 Massachusetts Institute of Technology
  34. 34.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New YorkMATHGoogle Scholar
  35. 35.
    Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice Hall International, Eagle CliffsMATHGoogle Scholar
  36. 36.
    Fukunaga K, Hostetler LD (1973). Optimization of k-nearest-neighbor density estimates. IEEE Trans Inform Theory IT-19:320–326CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2005

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, School of Information Science and EngineeringFudan UniversityShanghaiChina

Personalised recommendations