Skip to main content
SpringerLink
Log in

Rotation and Gray-Scale Invariant Classification of Textures Improved by Spatial Distribution of Features

  • Conference paper
Database and Expert Systems Applications (DEXA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3588))

Included in the following conference series:

Abstract

In this paper, we present a framework for texture descriptors based on spatial distribution of textural features. Our approach is based on the observation that regional properties of textures are well captured by correlations among local texture patterns. The proposed method has been evaluated through experiments using real textures, and has shown significant improvements in recognition rates.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pun, C.-M., Lee, M.-C.: Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification. IEEE Trans. Pattern Analysis and Machine Intelligence 25(5), 590–603 (2003)

    Article  Google Scholar 

  2. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)

    Article  Google Scholar 

  3. Porter, R., Canagarajah, N.: Robust Rotation-Invariant Texture Classification: Wavelet, Gabor Filter, and GMRF Based Schemes. IEE Proc.-Vision Image Signal Processing 144(3), 180–188 (1997)

    Article  Google Scholar 

  4. Hayley, G.M., Manjunath, B.M.: Rotation Invariant Texture Classification Using Modified Gabor Filters. In: Proc. Int’l Conf. Image Processing 1995, vol. 3, pp. 262–265 (1995)

    Google Scholar 

  5. Dimai, A.: Rotation Invariant Texture Description using General Moment Invariants and Gabor Filters. In: Proc. 11th Scandinavian Conf. Image Analysis 1999, June 1999, pp. 391–398 (1999)

    Google Scholar 

  6. Fountain, S.R., Tan, T.N., Baker, K.D.: A Comparative Study of Rotation Invariant Classification and Retrieval of Texture Images. In: Proc. Ninth British Machine Vision Conf., September 1998, pp. 266–275 (1998)

    Google Scholar 

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

    Google Scholar 

  8. Kashiyap, R.L., Khotanzad, A.: A Moel-Based Methods for Rotation Invariant Texture Classification. IEEE Trans. Pattern Analysis and Machine Intelligence 8, 472–481 (1986)

    Article  Google Scholar 

  9. Mao, J., Jain, A.K.: Texture Classification and Segmentation Using Multiresolution Simultaneous Autoregressive Models. Pattern Recognition 25, 173–188 (1992)

    Article  Google Scholar 

  10. Pietikainen, M., Ojala, T., Xu, Z.: Rotation-Invariant Texture Classification Using Feature Distributions. Pattern Recognition 33, 43–52 (2000)

    Article  Google Scholar 

  11. Pietikainen, M., Ojala, T., Xu, Z.: Efficient Rotation Invariant Texture Features for Content-Based Imae Retrieval. Pattern Recognition 31, 1725–1732 (1998)

    Article  Google Scholar 

  12. Greenspan, H., Belongie, S., Goodman, R., Perona, P.: Rotation Invariant Texture Recognition Using a Steerable Pyramid. In: Proc. 12th Int’l Conf. Pattern Recognition, vol. 2, pp. 162–167 (1994)

    Google Scholar 

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

    Article  Google Scholar 

  14. Leung, M., Peterson, A.M.: Scale and Rotation Invariant Texture Classification. In: Proc. 26th Int’l Conf. Acoustics, Speech, and Signal Processing, vol. 1, pp. 461–465 (1992)

    Google Scholar 

  15. Wu, Y., Yoshida, Y.: An Efficient Method for Rotation and Scaling Invariant Texture Classification. In: Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing, vol. 4, pp. 2519–2522 (1995)

    Google Scholar 

  16. Tan, T.N.: Scale and Rotation Invariant Texture Classification. In: IEE Colloquium Texture Classification: Theory and Applications (1994)

    Google Scholar 

  17. Manian, V., Vasquez, R.: Scaled and Rotated Texture Classification Using a Class of Basis Functions. Pattern Recognition 31, 1937–1948 (1998)

    Article  Google Scholar 

  18. Chen, J.-L., Kundu, A.: Rotation and Gray Scale Transform Invariant Texture Identification Using Wavelet Decomposition and Hidden Markov Model. IEEE Trans. Pattern Analysis and Machine Intelligence 16, 208–214 (1994)

    Article  Google Scholar 

  19. Wu, W.R., Wei, S.C.: Rotation and Gray-Scale Transform Invariant Texture Classification Using Spiral Resampling, Subband Decomposition, and Hidden Markov Model. IEEE Trans. Image Processing 5, 1423–1434 (1996)

    Article  Google Scholar 

  20. Chetverykov, D.: Texture Analysis Using Feature Based Pairwise Interaction Maps. Pattern Recognition 32(3), 487–502 (1999)

    Article  Google Scholar 

  21. Collection of Microtextures, Computer Vision Group, University of Bonn, http://www-dbv.cs.uni-bonn.de/image/browse

  22. Ojala, T., Maenpaa, T., Pietikainen, M.: Outex-A New Framework for Empirical Evaluation of Texture Analisis Algorithms. In: Proc. 16th Int’l Conf. Pattern Recognition (2002)

    Google Scholar 

  23. Haralick, R.M.: Statistical and Structural Approaches to Texture. Proceedings of the IEEE 67, 786–804 (1979)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Yanbian University of Science and Technology, Yanji, Jilin Province, China

    Gouchol Pok

  2. Department of Computer Science, Chungbuk National University, Cheongju, Chungbuk, Korea

    Keun Ho Ryu

  3. Department of Computer Science, Texas A&M University, College Station, Texas, USA

    Jyh-charn Lyu

Authors
  1. Gouchol Pok
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keun Ho Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jyh-charn Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Copenhagen Business School, Centre for Applied ICT, 60 Howitzvej, 2000, Frederiksberg, DK

    Kim Viborg Andersen

  2. University Of Technology Sydney, NSW 2007, Australia

    John Debenham

  3. University of Linz, Altenbergerstraße 69, 4040, Linz, Austria

    Roland Wagner

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pok, G., Ryu, K.H., Lyu, Jc. (2005). Rotation and Gray-Scale Invariant Classification of Textures Improved by Spatial Distribution of Features. In: Andersen, K.V., Debenham, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2005. Lecture Notes in Computer Science, vol 3588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546924_25

Download citation

  • DOI: https://doi.org/10.1007/11546924_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28566-3

  • Online ISBN: 978-3-540-31729-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation