Binary Co-occurrence Matrix in Image Database Indexing

  • Iivari Kunttu
  • Leena Lepistö
  • Juhani Rauhamaa
  • Ari Visa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


The use of the second order statistical measures has became popular in the image database indexing and retrieval. Unlike the common approach, image histogram, second order statistics like image correlogram and autocorrelogram consider also the spatial organization of the image colors or gray levels. Recently, correlograms and autocorrelograms have been widely used in the image database indexing. In this paper we present binary co-occurrence matrix, a new statistical measure for image indexing. This measure represents the footprint distribution of the co-occurrence matrix. Compared to image correlogram, this approach provides better retrieval accuracy at lower computational cost. We make retrieval experiments using two industrial image databases. These databases contain images collected from paper and metal manufacturing processes. In the experiments, we compare the retrieval performance of our approach to that of correlograms and autocorrelograms.


  1. 1.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, Addison Wesley, New York (1999)Google Scholar
  2. 2.
    Duda, R.O., Hart, P.E., Stork,.G.: Pattern Classification. 2nd ed. John Wiley (2001)Google Scholar
  3. 3.
    Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Massachusetts, USA (2001)Google Scholar
  4. 4.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3, 6 (1973)Google Scholar
  5. 5.
    Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R.: Image indexing Using Color Correlograms. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico (1997) 762–768Google Scholar
  6. 6.
    Kaizer, H.: A Quantification of Textures on Aerial Photographs. Tech. Note No. 121, A.69484. Boston University Research Laboratories, Boston University (1955)Google Scholar
  7. 7.
    Kunttu, I., Lepistö, L., Rauhamaa, J., Visa, A.: Image Retrieval without Segmentation. Proceedings of 10th Finnish AI Conference. Oulu, Finland (2002) 164–169Google Scholar
  8. 8.
    Kunttu, I., Lepistö, L., Rauhamaa, J., Visa, A.: Image Correlogram in Image Database Indexing and Retrieval. Proceedings of 4th European Workshop on Image Analysis for Multimedia Active Services, London, UK, (2003) 88–91Google Scholar
  9. 9.
    Ojala, T., Rautiainen, M., Matinmikko, E., Aittola, M.: Semantic Image Retrieval with HSV Correlograms. Proceedings of 12th Scandinavian Conference on Image Analysis. Bergen, Norway (2001) 621–637Google Scholar
  10. 10.
    Rauhamaa, J., Reinius, R.: Paper Web Imaging with Advanced Defect Classification. TAPPI Technology Summit, Atlanta, Georgia (2002)Google Scholar
  11. 11.
    Smith, J.R., Chang, S.F.: Tools and Techniques for Color Image Retrieval. Storage and Retrieval for Image and Video Databases IV, SPIE Proceedings, Vol. 2670 (1996) 1630–1639Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Iivari Kunttu
    • 1
  • Leena Lepistö
    • 1
  • Juhani Rauhamaa
    • 2
  • Ari Visa
    • 1
  1. 1.Institute of Signal ProcessingTampere University of TechnologyTampereFinland
  2. 2.ABB Oy, Paper, Printing, Metals & Minerals, AutomationHelsinkiFinland

Personalised recommendations