Skip to main content

Optimal Discrete Wavelet Frames Features for Texture-Based Image Retrieval Applications

  • Conference paper
Visual Informatics: Bridging Research and Practice (IVIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5857))

Included in the following conference series:

Abstract

In this paper, experiments were conducted to find the optimal configuration for discrete wavelet frames texture feature extraction method for use in real-time content-based image retrieval application. Several parameters of the algorithm such as the wavelet basis, the number of decomposition levels, and the distance metric are evaluated in terms of retrieval performance, and the optimum value for each parameter is suggested. By experimenting on the statistical function as well as channel selection, the final DWF configuration is proposed that achieves an average of more than 80% accuracy using the Brodatz texture dataset and about 70% accuracy using the VisTex dataset.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing 4, 1549–1560 (1995)

    Article  Google Scholar 

  2. Chen, T., Ma, K.-K., Chen, L.-H.: Discrete wavelet frame representations of color texture features for image query. In: Proceedings of IEEE Second Workshop on Multimedia Signal Processing, pp. 45–50 (1998)

    Google Scholar 

  3. Liapis, S., Alvertos, N., Tziritas, G.: Maximum likelihood texture classification and Bayesian texture segmentation using discrete wavelet frames. In: Proceedings of 13th International Conference on Digital Signal Processing, pp. 1107–1110 (1997)

    Google Scholar 

  4. Liapis, S., Tziritas, G.: Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Transactions on Multimedia 6, 676–686 (2004)

    Article  Google Scholar 

  5. Depeursinge, A., Sage, D., Hidki, A., Platon, A., Poletti, P.-A., Unser, M., Muller, H.: Lung Tissue Classification Using Wavelet Frames. In: Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6259–6262 (2007)

    Google Scholar 

  6. Mallat, S.G.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)

    Article  MATH  Google Scholar 

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

    Google Scholar 

  8. Picard, R., et al.: Vision Texture 1.0, MIT Media Laboratory (1995), http://www-white.media.mit.edu/vismod/imagery/VisionTexture/vistex.html

  9. Fauzi, M.F.A.: Content-based image retrieval of museum images. PhD Thesis, University of Southampton (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ahmad Fauzi, M.F. (2009). Optimal Discrete Wavelet Frames Features for Texture-Based Image Retrieval Applications. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds) Visual Informatics: Bridging Research and Practice. IVIC 2009. Lecture Notes in Computer Science, vol 5857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05036-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-05036-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05035-0

  • Online ISBN: 978-3-642-05036-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics