Nothing Special   »   [go: up one dir, main page]

Skip to main content

Selecting a Discriminant Subset of Co-occurrence Matrix Features for Texture-Based Image Retrieval

  • Conference paper
Advances in Visual Computing (ISVC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3804))

Included in the following conference series:

Abstract

In the general case, searching for images in a content-based image retrieval (CBIR) system amounts essentially, and unfortunately, to a sequential scan of the whole database. In order to accelerate this process, we want to generate summaries of the image database. In this paper, we focus on the selection of the texture features that will be used as a signature in our forthcoming system. We analysed the descriptors extracted from grey-level co-occurrence matrices’s (COM) under the constraints imposed by database systems.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7, 11–32 (1991)

    Article  Google Scholar 

  2. Ma, W.Y., Manjunath, B.S.: NETRA: A toolbox for navigating large image databases. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 1997 (1997)

    Google Scholar 

  3. Rui, Y., Huang, T.: Image retrieval: Current techniques, promising directions and open issues. Journal of Visual Communication and Image representation (1999)

    Google Scholar 

  4. Lin, H.C., Wang, L., Yang, S.: Extracting periodicity of a regular texture based on autocorrelation function. Pattern Recognition Letters 18, 433–443 (1997)

    Article  Google Scholar 

  5. Tamura, H., Mori, S., Yamawaki, T.: Texture features corresponding to visual perception. IEEE Transaction on Systems, Man and Cybernetics 8, 460–473 (1978)

    Article  Google Scholar 

  6. Stand, J., Taxt, T.: Local frequency features for texture classification. Pattern Recognition 27, 1397–1406 (1994)

    Article  Google Scholar 

  7. Randen, T., Husøy, J.: Filtering for texture classification: A comparative study. IEEE Transaction on Pattern Analysis and Machine Intelligence 21, 291–310 (1999)

    Article  Google Scholar 

  8. Buf, J., Kardan, H., Spann, M.: Texture feature performance for image segmentation. Pattern Recognition 23, 291–309 (1990)

    Article  Google Scholar 

  9. Haralick, R., Shanmugan, K., Dinstein, I.: Textural features for image classification. IEEE Trans. on Systems, Man, and Cybernetics SMC-3, 610–621 (1973)

    Article  Google Scholar 

  10. Conners, R., Harlow, C.: A theorical comparison of texture algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2, 204–222 (1980)

    Article  MATH  Google Scholar 

  11. Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The r*-tree: An efficient and robust access method for points and rectangles. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 322–331 (1990)

    Google Scholar 

  12. Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 47–54 (1984)

    Google Scholar 

  13. Berchtold, S., Keim, D.A., Kriegel, H.P.: The X-tree: An index structure for high-dimensional data. In: 22nd International Conference on Very Large Data Bases (VLDB), Mumbai (Bombay), India, pp. 28–39 (1996)

    Google Scholar 

  14. Katayama, N., Satoh, S.: The SR-tree: an index structure for high-dimensional nearest neighbor queries. In: ACM International Conference on Management of Data (SIGMOD), Tucson, Arizona, pp. 369–380 (1997)

    Google Scholar 

  15. (Brodatz’s Texture Album), http://www.ux.his.no/~tranden/brodatz.html

  16. (Meastex database), http://ccsip.elec.uq.edu.au/~guy/meastex/meastex.html

  17. Smith, G., Burns, I.: Measuring texture classification algorithms. Pattern Recognition Letters 18, 1495–1501 (1997)

    Article  MATH  Google Scholar 

  18. Smeulders, A., Worring, M., Santini, S., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1349–1380 (2000)

    Article  Google Scholar 

  19. Loisant, E., Saint-Paul, R., Martinez, J., Raschia, G., Mouaddib, N.: Browsing clusters of similar images. In: Acte des 19e Journées Bases de Données Avancées (BDA 2003), Lyon, France, pp. 109–128 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Idrissi, N., Martinez, J., Aboutajdine, D. (2005). Selecting a Discriminant Subset of Co-occurrence Matrix Features for Texture-Based Image Retrieval. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_88

Download citation

  • DOI: https://doi.org/10.1007/11595755_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30750-1

  • Online ISBN: 978-3-540-32284-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics