Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector

  • Pablo Suau
  • Francisco Escolano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4478)

Abstract

In this paper we propose a Bayesian filter for the Kadir Scale Saliency Detector. Such filter is addressed to deal with the main bottleneck of the Kadir detector, which is the scale space search for all pixels in the image. Given some statistical knowledge about images considered, we show that it is possible to discard some points before applying the Kadir detector by using Information Theory and Bayesian Analysis, increasing efficiency with low error. Our method is based on the intuitive idea that homogeneous (not salient) image regions at high scales probably will be also homogeneous at lower scales of scale space.

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References

  1. 1.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.A.: A Comparison of Affine Region Detectors. International Journal of Computer Vision 65(1–2), 43–72 (2005)CrossRefGoogle Scholar
  2. 2.
    Kadir, T., Brady, M.: Saliency, Scale and Image Description. International Journal of Computer Vision 45(2), 83–105 (2001)MATHCrossRefGoogle Scholar
  3. 3.
    Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), Madison, WI, USA, pp. 264–271 (2003)Google Scholar
  4. 4.
    Oikonomopoulos, A., Patras, I., Pantic, M.: Kernel-based recognition of human actions using spatiotemporal salient points. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), New York, NY, USA, pp. 151–151 (2006)Google Scholar
  5. 5.
    Konishi, S., Yuille, A.L., Coughlan, J.M., Zhu, S.C.: Statistical Edge Detection: Learning and Evaluating Edge Cues. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 57–74 (2003)CrossRefGoogle Scholar
  6. 6.
    Carneiro, G., Jepson, A.D.: The Distinctiveness, Detectability, and Robustness of Local Image Features. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA, pp. 296–301 (2005)Google Scholar
  7. 7.
    Guilles, S.: Robust Description and Matching of Images, Ph. D. Thesis, University of Oxford (1998)Google Scholar
  8. 8.
    Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 228–241. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Cover, T.M., Thomas, J.S.: Elements of Information Theory. Wiley Interscience, Hoboken (1991)MATHGoogle Scholar
  10. 10.
    Cazorla, M., Escolano, F.: Two Bayesian methods for junction classification. IEEE Transactions on Image Processing 12(3), 317–327 (2003)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Pablo Suau
    • 1
  • Francisco Escolano
    • 1
  1. 1.Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Ap. de correos 99, 03080, AlicanteSpain

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