Skip to main content
SpringerLink
Log in
Book cover

Pattern Recognition Theory and Applications pp 21–33Cite as

Texture Subspaces

  • Conference paper

Part of the book series: NATO ASI Series ((NATO ASI F,volume 30))

Abstract

The subspace method of pattern recognition has been developed for fast and accurate classification of high-dimensional feature vectors, especially power spectra and distribution densities. The basic algorithms for class subspace construction are statistically motivated, and the classification is based on inner products. In texture analysis, this method has been previously applied for two-dimensional spatial frequency spectra. In this work we show that a feasible method for texture window classification is to use a smoothed cooccurrence matrix as the feature vector and to define texture classes for such representations by the subspace method. These texture subspaces seem to capture the characteristic second-order properties of texture fields. Results are given using various natural and synthetic textures.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akhiezer, N.I. and Glazman, I.M.: Theory of Linear Operators in Hilbert Space, Vol. I, Pitman Adv. Publ. ProgrBoston, 1981.

    Google Scholar 

  2. Brodatz, P.: Textures: A Photographic Album for Artists and Designers, Reinhold, New York, 1968.

    Google Scholar 

  3. Caelli, T. and Julesz, B.: On Perceptual Analyzers Underlying Visual Texture Discrimination: Part I, Part II, Biol. Cyb. 28, 1978, pp. 167–175 and 29, 1978, pp. 201–214.

    Article  Google Scholar 

  4. Conners, R.W. and Harlow, C.A.: A Theoretical Comparison of Texture Algorithms, IEEE Trans. on Patt. Anal, and Mach. Intel., PAMI- 2, 1980, pp. 204–222.

    Article  MATH  Google Scholar 

  5. Conners, R.W., Trivedi, M.M., and Harlow, C.A.: Segmentation of a High Resolution Urban Scene Using Texture Operators, Comp. Vision, Graphics and Image Proc. 25, 1984, pp. 273–310.

    Article  Google Scholar 

  6. D’Astous, F. and Jernigan, M.E.: Texture Discrimination Based on Detailed Measures of the Power Spectrum, Proc. 7th ICPR, Montreal, July 30 — Aug. 2, 1984, pp. 83–86.

    Google Scholar 

  7. Diday, E. and Simon, J.C.: Clustering Analysis, in K.S. Fu (Ed.), Digital Pattern Recognition, Springer, Berlin-Heidelberg-New York, 1976, pp. 47–94.

    Chapter  Google Scholar 

  8. Duvernoy, J.: Optical-Digital Processing of Directional Terrain Textures Invariant under Translation, Rotation, and Change of Scale, Appl. Optics 23, No. 6, 1984, pp. 828–837.

    Article  Google Scholar 

  9. Haralick, R.M.: Statistical and Structural Approaches to Texture,Proc. IEEE 67, 1979, pp. 786–804.

    Article  Google Scholar 

  10. Haralick, R.M., Shanmugam, K., and Dinstein, I.: Textural Features for Image Classification, IEEE Trans. Syst. Man Cybern., SMC-3, 1973, pp. 610–621.

    Article  Google Scholar 

  11. Iijima, T.: A Theory of Pattern Recognition by Compound Similarity Method (in Japanese), Trans. IECE Japan, PRL 74–25.

    Google Scholar 

  12. Julesz, T.: Visual Pattern Discrimination, IRE Trans. Inform. Theory IT- 8, 1962, pp. 84–92.

    Article  Google Scholar 

  13. Kittler, J. and Young, P.C.: Discriminant Function Implementation of a Minimum Risk Classifier, Biol. Cyb. 18, 1975, pp. 169–179.

    Article  MATH  Google Scholar 

  14. Kohonen, T., Nemeth, G., Bry, K.-J., Jalanko, M., and Makisara, K.: Spectral Classification of Phonemes by Learning Subspaces, Proc. 1979 IEEE Int. Conf. on Acoust., Speech and Signal Proc., April 2–4, 1979, Washington, DC, pp. 97–100.

    Google Scholar 

  15. Kohonen, T., Rüttinen., Jalanko, M., Reuhkala, E., and Haltsonen, S.: A 1000 Word Recognition System Based on the Learning Subspace Method and Redundant Hash Addressing, Proc. 5th Int. Conf. on Pattern Recognition, Miami Beach, FL, Dec. 1.-4., 1980, pp. 158–165.

    Google Scholar 

  16. Lendaris,G. and Stanley,G.: Diffraction Pattern Samplings for Automatic Pattern Recognition, Proc. IEEE 58, 1970, pp. 198–216.

    Article  Google Scholar 

  17. Oja, E.: Subspace Methods of Pattern Recognition, Research Studies Press, Letch- worth and J. Wiley, New York, 1983.

    Google Scholar 

  18. Oja, E. and Kuusela, M.: The ALSM Algorithm — an Improved Subspace Method of Classification, Patt. Rec. 16, No. 4, 1983, pp. 421–427.

    Article  Google Scholar 

  19. Oja, E. and Karhunen, J.: An Analysis of Convergence for a Learning Version of the Subspace Method, J.Math. Anal. Appl. 91, 1983, pp. 102–111.

    Article  MathSciNet  MATH  Google Scholar 

  20. Oja, E. and Parkkinen, J.: On Subspace Clustering, Proc. 7th. Int. Conf. on Pattern Recognition, Montreal, Canada, July 30.— Aug. 2., 1984, pp. 692–695.

    Google Scholar 

  21. Parkkinen, J. and Oja, E.: Texture Classification by the Subspace Method,Proc. 4th. Scand. Conf on Image Analysis,Trondheim, Norway June 17.-20.,1985, pp. 429–436.

    Google Scholar 

  22. Rüttinen, H.: Recognition of Phonemes in a Speech Recognition System Using Learning Projective Methods, Dr.Tech. Thesis, Helsinki University of Technology, 1986.

    Google Scholar 

  23. Unser, M.: A Fast Texture Classifier Based on Cross Entropy Minimization, in H.W. Schüssler (Ed.), Signal Processing II: Theories and Applications, Elsevier Sci. Publ., 1983, pp. 261–264.

    Google Scholar 

  24. Van Gool, L., Dewaele, P. and Oosterlinck, A.: Texture Analysis Anno 1983, Comp. Vision, Graphics and Image Proc. 29, 1985, pp. 336–357.

    Article  Google Scholar 

  25. Vickers, A.L. and Modestino, J.W.: A Maximum Likelihood Approach to Texture Classification, IEEE Trans, on Pattern Anal, and Machine Intelligence, PAMI- 4 1982, pp. 61–68.

    Article  Google Scholar 

  26. Watanabe, S.: Knowing and Guessing — a Quantitative Study of Inference and Information, J. Wiley, New York, 1969.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Mathematics, Kuopio University, POB 6, 70211, Kuopio, Finland

    Erkki Oja & Jussi Parkkinen

Authors
  1. Erkki Oja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jussi Parkkinen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Philips Research Laboratory, Avenue Em. Van Becelaere 2, Box 8, B-1170, Brussels, Belgium

    Pierre A. Devijver

  2. Department of Electronic and Electrical Engineering, University of Surrey, GU2 5XH, Guildford, UK

    Josef Kittler

Rights and permissions

Reprints and permissions

Copyright information

© 1987 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Oja, E., Parkkinen, J. (1987). Texture Subspaces. In: Devijver, P.A., Kittler, J. (eds) Pattern Recognition Theory and Applications. NATO ASI Series, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83069-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-83069-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-83071-6

  • Online ISBN: 978-3-642-83069-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation