Average Common Submatrix: A New Image Distance Measure

  • Alessia Amelio
  • Clara Pizzuti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

A new information-theoretic distance measure for images is proposed. The measure is based on the concept of average common sub-matrix by considering the pixel matrices associated with the images. An algorithm to compute such a value is described, and its computational complexity analyzed. Experimental results show that the measure is able to discriminate images by correctly reflecting human perception. Furthermore, comparison with state-of-the-art information-theoretic measures, points out that the new measure outperforms these measures in terms of retrieval precision.

Keywords

image retrieval similarity measure pattern matching 

References

  1. 1.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)CrossRefMATHGoogle Scholar
  2. 2.
    Crochemore, M., Gasieniec, L., Rytter, W., Plandowski, W.: Two-dimensional pattern matching in linear time and small space. In: Mayr, E.W., Puech, C. (eds.) STACS 1995. LNCS, vol. 900, pp. 181–192. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  3. 3.
    Giancarlo, R.: A generalization of the suffix tree to square matrices, with applications. SIAM Journal on Computing 24(3), 520–562 (1995)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Huttenlocher, D., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)CrossRefGoogle Scholar
  5. 5.
    Pratt, W.K.: Digital Image Processing. Wiley, New York (1991)MATHGoogle Scholar
  6. 6.
    Tourassi, G.D., Harrawood, B.: Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammogragrams. Medical Physics 34(1), 140–150 (2007)CrossRefGoogle Scholar
  7. 7.
    Ulitsky, I., Burstein, D., Tuller, T., Chor, B.: The average common substring approach to phylogenomic reconstruction. Journal of Computational Biology 13(2), 336–350 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Wang, L., Zhang, Y., Feng, J.: On the euclidean distance of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(8), 1334–1339 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alessia Amelio
    • 1
  • Clara Pizzuti
    • 1
  1. 1.Institute for High Performance Computing and Networking (ICAR)National Research Council of Italy (CNR)Italy

Personalised recommendations