Skip to main content
Springer Nature Link
Log in

Texture Classification Using the Lempel-Ziv-Welch Algorithm

  • Conference paper
Advances in Artificial Intelligence – SBIA 2004 (SBIA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3171))

Included in the following conference series:

Abstract

This paper presents a new, simple and efficient texture classification method using Lempel-Ziv-Welch (LZW) compression algorithm. In the learning stage, LZW algorithm constructs dictionaries for the horizontal and vertical structure of each class. In the classification stage, texture samples to be classified are encoded by LZW in static mode, using the dictionaries constructed in the learning stage, in vertical and horizontal scanning order. A sample is assigned to the class whose dictionaries minimize the average horizontal and vertical coding rate. The classifier was evaluated for various sample sizes and training set sizes, using 30 Brodatz textures. The proposed method correctly classified 100% of 3000 Brodatz texture samples, and direct comparisons indicated the superiority of the method over several high performance classifiers.

This work was supported by CNPQ, a governmental Brazilian institution dedicated to scientific and technological development.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Porat, M., Zeevi, Y.Y.: Localized Texture Processing in Vision: Analysis and Synthesis in the Gaborian Space. IEEE Transactions on Biomedical Engineering 36(1), 115–129 (1989)

    Article  Google Scholar 

  2. Dell’Acqua, F., Gamba, P.: Texture-based Characterization of Urban Environments on Satellite SAR Images. IEEE Transactions on Geoscience and Remote Sensing 41(1), 153–159 (2003)

    Article  Google Scholar 

  3. Augusteijn, M.F., Clemens, L.E., Shaw, K.A.: Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier. IEEE Transactions on Geoscience and Remote Sensing 33(3), 616–626 (1995)

    Article  Google Scholar 

  4. Southard, T.E., Southard, K.A.: Detection of Simulated Osteoporosis in Maxillae Using Radiographic Texture Analysis. IEEE Transactions on Biomedical Engineering 43(2), 123–132 (1996)

    Article  Google Scholar 

  5. Kumar, A., Pang, G.K.H.: Defect Detection in Textured Materials Using Optimized Filters Systems. IEEE Transactions on Man and Cybernetics, Part B 32(5), 553–570 (2002)

    Article  Google Scholar 

  6. Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)

    Article  Google Scholar 

  7. He, X., Hu, Y., Zhang, H., Li, M., Cheng, Q., Yan, S.: Bayesian Shape Localization for Face Recognition Using Global and Local Textures. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 102–113 (2004)

    Article  Google Scholar 

  8. Baheerathan, S., Albregtsen, F., Danielsen, H.E.: New Texture Features Based on the Complexity Curve. Pattern Recognition 32(4), 605–618 (1999)

    Article  Google Scholar 

  9. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, New York (2000)

    Google Scholar 

  10. Bovik, A. (ed.): Handbook of Image and Video Processing. Academic Press, San Diego London (2000)

    MATH  Google Scholar 

  11. Cohen, F.S., Fan, Z., Patel, M.A.: Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(2), 192–202 (1991)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. du Buf, J.M.H., Kardan, M., Spann, M.: Texture Feature Performance for Image Segmentation. Pattern Recognition 23(3-4), 291–309 (1990)

    Article  Google Scholar 

  14. Li, S., Kwok, J.T., Zhu, H., Wang, Y.: Texture Classification Using the Support Vector Machines. Pattern Recognition 36(12), 2883–2893 (2003)

    Article  MATH  Google Scholar 

  15. Bell, T.C., Cleary, J.G., Witten, J.H.: Text Compression. Prentice-Hall, Englewood Cliffs (1990)

    Google Scholar 

  16. Ziv, J., Lempel, A.: Compression of Individual Sequences via Variable-Rate Coding. IEEE Transactions on Information Theory 24(5), 530–536 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  17. Ziv, J.: On Classification with Empirically Observed Statistics and Universal Data Compression. IEEE Transactions on Information Theory 34(2), 278–286 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  18. Ziv, J., Merhav, N.: A Measure of Relative Entropy Between Individual Sequences with Application to Universal Classification. IEEE Transactions on Information Theory 39(4), 1270–1279 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  19. Frank, E., Chui, C., Witten, I.H.: Text Categorization Using Compression Models. In: Proceedings of the Data Compression Conference - DCC 2000, Salt Lake City, pp. 555–555 (2000)

    Google Scholar 

  20. Teahan, W.J., Harper, D.J.: Using Compression Based Language Models for Text Categorization. In: Callan, J., Croft, B., Lafferty, J. (eds.) Workshop on Language Modeling and Information Retrieval, ARDA 2001, pp. 83–88 (2001)

    Google Scholar 

  21. Welch, T.A.: A Technique for High Performance Data Compression. IEEE Computer 17(6), 8–19 (1984)

    Google Scholar 

  22. Shannon, C.E.: A Mathematical Theory of Communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    MATH  MathSciNet  Google Scholar 

  23. Savari, S.A.: Redundancy of the Lempel-Ziv-Welch Code. In: Proceedings Data Compression Conference, 1997. DCC 1997, March 25-27, pp. 191–200 (1997)

    Google Scholar 

  24. Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Informática, CCEN, UFPB, Cidade Universitária, Campus I, 58.051-900, João Pessoa, PB, Brazil

    Leonardo Vidal Batista & Moab Mariz Meira

Authors
  1. Leonardo Vidal Batista
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moab Mariz Meira
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Instituto de Informática, UFRGS, Porto Alegre, RS, Brasil

    Ana L. C. Bazzan

  2. Intelligent Systems Laboratory LSI, Center of Technology, Federal University of Maranao UFMA, Bacanga Campus, 65080-040, Sao Luis, MA, Brazil

    Sofiane Labidi

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Batista, L.V., Meira, M.M. (2004). Texture Classification Using the Lempel-Ziv-Welch Algorithm. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-28645-5_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23237-7

  • Online ISBN: 978-3-540-28645-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics