Skip to main content
SpringerLink
Log in

Spatial and Frequency Domain–Based Feature Fusion Method for Texture Retrieval

  • Conference paper
Intelligent Data analysis and its Applications, Volume I

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 297))

  • 1847 Accesses

Abstract

This work presents a novel feature fusion method for texture retrieval. Considering the advantages of both the spatial and frequency domain, we first carry on the experiments in spatial domain and frequency domain respectively. On one hand, sober and histogram feature are used to calculate the similarity. On the other hand, Fourier is applied to obtain the frequency feature. Then a feature fusion scheme is used to join the two features came from spatial and frequency domain. Experimental results on MIT texture database show that the proposed method is effective.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Haralick, R.M., Shanmugam, K., Distein, I.: Textural features for image classification. IEEE Trans. on Systems, Man, and Cybernetics 3(6), 610–621 (1973)

    Article  Google Scholar 

  2. Soh, K.S., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-ocuurrence matrices. IEEE Trans. on Geoscience and Remote Sensing 37(2), 780–795 (1999); Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Survey 34(4), 399–485 (2003)

    Google Scholar 

  3. Ulaby, F.T., Kouyate, F., Brisco, B., et al.: Textural information in SAR images. IEEE Transactions on Geoscience and Remote Sensing 24(2), 235–245 (1986); Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Upper Saddle River (2002)

    Google Scholar 

  4. Baraldi, A., Parmiggiani, F.: An investigation of the textual characteristics associated with gray level co-ocurrence matrix statistical parameters. IEEE Trans. on Geoscience and Remote Sensing 33(2), 293–304 (1995)

    Article  Google Scholar 

  5. Hua, B., Ma, F.-L., Jiao, L.-C.: Research on computation of GLCM of image texture. Acta Electronica Sinica 34(1), 155–158 (2006)

    Google Scholar 

  6. Clausi, D.A., Jernigan, M.E.: A fast method to determine co-occurrence texture features. IEEE Trans. on Geoscience and Remote Sensing 36(1), 298–300 (1998)

    Article  Google Scholar 

  7. Walker, R.F., Jackway, P.T., Longstaff, I.D.: Recent developments in the use of co-occurrence matrix for texture recognition. In: Proceedings of IEEE Conference on Digital Signal Processing, Santorini, Greece, vol. 1, pp. 63–65 (1997)

    Google Scholar 

  8. Kandaswamy, U., Adjeroh, D.A., Lee, M.C.: Efficient texture analysis of SAR imagery. IEEE Trans. on Geoscience and Remote Sensing 43(9), 2075–2083 (2005)

    Article  Google Scholar 

  9. German, S., German, D.: Stochastic relaxation, gibbs distribution and Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6(6), 721–741 (1984)

    Article  Google Scholar 

  10. McCormick, B.H., Jayaramamurthy, S.N.: Time series model for texture synthesis. International Journal of Computer Information Science 3(4), 329–343 (1974)

    Article  MATH  Google Scholar 

  11. Ng, I., Tan, T., Kittler, J.: On local linear transform and Gabor filter representation of texture. In: Proceeding of the 11th IAPR Conference on Image, Speech and Signal Analysis, Hague, Netherlands, pp. 627–631 (1992)

    Google Scholar 

  12. Zhou, F., Feng, J., Shi, Q.: Image segmentation based on local fourier transform. In: Proceedings of International Conference on image Processing, Wuhan, China, pp. 610–613 (2001)

    Google Scholar 

  13. Dunn, D., Higgins, W.E., Wakeley, J.: Texture segmentation using 2-D Gabor elementary functions. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(2), 130–149 (1994)

    Article  Google Scholar 

  14. Pavlidis, T.: Structural Descriptions and Graph Grammars, pp. 86–103. Springer, Berlin (1980)

    Google Scholar 

  15. Soille, P.: Morphological Image Analysis: Principles and Applications, pp. 289–317. Springer Press, Berlin (2003)

    Google Scholar 

  16. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions in Information Theory IT-13, 21–27 (1967)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Yangtze University, Wuhan, China

    Rurui Zhou

Authors
  1. Rurui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rurui Zhou .

Editor information

Editors and Affiliations

  1. National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  2. Department of Computer Science Faculty of Elec. Eng. & Comp. Sci., VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Vaclav Snasel

  3. Departamento de Informáca y Automática, Facultad de Biología, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  5. Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan

    Shyue-Liang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhou, R. (2014). Spatial and Frequency Domain–Based Feature Fusion Method for Texture Retrieval. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07776-5_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07775-8

  • Online ISBN: 978-3-319-07776-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation