An Attention Based Similarity Measure for Colour Images

  • Li Chen
  • F. W. M. Stentiford
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Much effort has been devoted to visual applications that require effective image signatures and similarity metrics. In this paper we propose an attention based similarity measure in which only very weak assumptions are imposed on the nature of the features employed. This approach generates the similarity measure on a trial and error basis; this has the significant advantage that similarity matching is based on an unrestricted competition mechanism that is not dependent upon a priori assumptions regarding the data. Efforts are expended searching for the best feature for specific region comparisons rather than expecting that a fixed feature set will perform optimally over unknown patterns. The proposed method has been tested on the BBC open news archive with promising results.


Colour Image Visual Attention Image Retrieval Retrieval Performance Similarity Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)MATHCrossRefGoogle Scholar
  2. 2.
    Fu, H., Chi, Z., Feng, D.: Attention-driven image interpretation with application to image retrieval. Pattern Recognition 39(7) (2006)Google Scholar
  3. 3.
    Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. on Image Processing 13(10), 1304–1318 (2004)CrossRefGoogle Scholar
  4. 4.
    Treisman, A.: Preattentive processing in vision. In: Pylyshyn, Z. (ed.) Computational Processes in Human Vision: an Interdisciplinary Perspective, Ablex Publishing Corp., Norwood (1988)Google Scholar
  5. 5.
    Tsotsos, J.K., Culhane, S.M., Wai, W.Y.K., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial Intelligence 78, 507–545 (1995)CrossRefGoogle Scholar
  6. 6.
    Stentiford, F.W.M.: An attention based similarity measure with application to content based information retrieval. In: Yeung, M.M., Lienhart, R.W., Li, C.-S. (eds.) Storage and Retrieval for Media Databases, Proc SPIE, vol. 5021, pp. 221–232 (2003)Google Scholar
  7. 7.
    Stentiford, F.W.M.: Attention based similarity. Pattern Recognition (2006) (in press)Google Scholar
  8. 8.
    Desimone, R.: Visual attention mediated by biased competition in extrastriate visual cortex. Phil. Trans. R. Soc. Lond. B 353, 1245–1255 (1998)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Multimedia Understanding through Semantics, Computation and Learning, EC 6th Framework Programme. FP6-507752 (2005),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Li Chen
    • 1
  • F. W. M. Stentiford
    • 1
  1. 1.University College LondonUK

Personalised recommendations