Supervised Texture Classification Using a Novel Compression-Based Similarity Measure

  • Mehrdad J. Gangeh
  • Ali Ghodsi
  • Mohamed S. Kamel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)


Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)simil-arity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimensional MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes. Experimental results show that the proposed approach significantly improves the performance of supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures as well as approaches performed in feature space. It also improves the computation speed by about 40% compared to its rivals.


Discrete Cosine Transform Patch Size Local Binary Pattern Texture Image Kolmogorov Complexity 
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  1. 1.
    Petrou, M., Sevilla, P.G.: Image Processing Dealing with Texture. John Wiley & Sons, West Sussex (2006)CrossRefGoogle Scholar
  2. 2.
    Garcia, M., Puig, D.: Supervised texture classification by integration of multiple texture methods and evaluation windows. Image and Vision Computing 25(7), 1091–1106 (2007)CrossRefGoogle Scholar
  3. 3.
    Randen, T., Husøy, J.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Analysis and Machine Intelligence 21(4), 291–310 (1999)CrossRefGoogle Scholar
  4. 4.
    Melendez, J., Puig, D., Garcia, M.: Multi-level pixel-based texture classification through efficient prototype selection via normalized cut. Pattern Recognition 43(12), 4113–4123 (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Mirmehdi, M., Xie, X., Suri, J.: Handbook of Texture Analysis. Imperial Collage Press, London (2008)CrossRefGoogle Scholar
  6. 6.
    Ahonen, T., Pietikainen, M.: Image description using joint distribution of filter bank responses. Pattern Recognition Letters 30(4), 368–376 (2009)CrossRefGoogle Scholar
  7. 7.
    Li, M., Chen, X., Li, X., Ma, B., Vitányi, P.: The similarity metric. IEEE Trans. Information Theory 50(12), 3250–3264 (2004)CrossRefGoogle Scholar
  8. 8.
    Cilibrasi, R., Vitányi, P.: Clustering by compression. IEEE Trans. Information Theory 51(4), 1523–1545 (2005)CrossRefGoogle Scholar
  9. 9.
    Mortensen, J., Wu, J.J., Furst, J., Rogers, J., Raicu, D.: Effect of Image Linearization on Normalized Compression Distance. In: Ślęzak, D., Pal, S.K., Kang, B.-H., Gu, J., Kuroda, H., Kim, T.-H. (eds.) SIP 2009. CCIS, vol. 61, pp. 106–116. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Macedonas, A., Besiris, D., Economou, G., Fotopoulos, S.: Dictionary based color image retrieval. Journal of Visual Communication and Image Representation 19(7), 464–470 (2008)CrossRefGoogle Scholar
  11. 11.
    Cerra, D., Mallet, A., Gueguen, L., Datcu, M.: Algorithmic information theory-based analysis of earth observation images: An assessment. IEEE Geoscience and Remote Sensing Letters 7(1), 8–12 (2010)CrossRefGoogle Scholar
  12. 12.
    Vázquez, P., Marco, J.: Using normalized compression distance for image similarity measurement: an experimental study. The Visual Computer, 1–22 (2011)Google Scholar
  13. 13.
    Ghanbari, M.: Standard Codecs: Image Compression to Advanced Video Coding. The Institution of Electrical Engineers, London (2003)CrossRefGoogle Scholar
  14. 14.
    Campana, B., Keogh, E.: A compression-based distance measure for texture. Statistical Analysis and Data Mining 3(6), 381–398 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mehrdad J. Gangeh
    • 1
  • Ali Ghodsi
    • 2
  • Mohamed S. Kamel
    • 1
  1. 1.Center for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer EngineeringUniversity of WaterlooCanada
  2. 2.Department of Statistics and Actuarial ScienceUniversity of WaterlooCanada

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