Video Spatio-temporal Signatures Using Polynomial Transforms

  • Carlos Joel Rivero-Moreno
  • Stéphane Bres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3736)

Abstract

In this paper we integrate spatial and temporal information, which are extracted separately from a video sequence, for indexing and retrieval purposes. We focus on two filter families that are suitable models of the human visual system for spatial and temporal information encoding. They are special cases of polynomial transforms that perform local decompositions of a signal. Spatial primitives are extracted using Hermite filters, which agree with the Gaussian derivative model of receptive field profiles. Temporal events are characterized by Laguerre filters, which preserve the causality constraint in the temporal domain. Integration of both models gives a spatio-temporal feature extractor based on early vision.. Results encourage our model for video indexing and retrieval.

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References

  1. 1.
    Abramowitz, M., Stegun, I.A.: Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th printing. Dover, New York (1972)MATHGoogle Scholar
  2. 2.
    Ahanger, G., Little, T.D.C.: A Survey of Technologies for Parsing and Indexing Digital Video. Journal Visual Communication Image Representation 7(1), 28–43 (1996)CrossRefGoogle Scholar
  3. 3.
    Belt, H.J.W., den Brinker, A.C.: Optimality Condition for Truncated Generalized Laguerre Networks. Int. Journal Circuit Theory and Applications. 23, 227–235 (1995)MATHCrossRefGoogle Scholar
  4. 4.
    den Brinker, A.C., Roufs, J.A.J.: Evidence for a Generalized Laguerre Transform of Temporal Events by the Visual System. Biological Cybernetics 67, 395–402 (1992)MATHCrossRefGoogle Scholar
  5. 5.
    Cheung, S.C., Zakhor, A.: Estimation of Web Video Multiplicity. In: Proc. SPIE – Internet Imaging, vol. 3964, pp. 34–36 (2000)Google Scholar
  6. 6.
    Dağtaş, S., Al-Khatib, W., Ghafoor, A., Kashyap, R.L.: Models for Motion-Based Video Indexing and Retrieval. IEEE Trans. Image Processing. 9(1), 88–101 (2000)CrossRefGoogle Scholar
  7. 7.
    Del Bimbo, A.: Visual Information Retrieval. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  8. 8.
    Flickner, M., et al.: Query by Image and Video Content: The QBIC System. IEEE Computer 28(9), 23–32 (1995)Google Scholar
  9. 9.
    Idris, F., Panchanathan, S.: Review of Image and Video Indexing Techniques. Journal Visual Communication Image Representation 8(2), 146–166 (1997)CrossRefGoogle Scholar
  10. 10.
    Koekoek, R., Swarttouw, R.F.: The Askey-scheme of Hypergeometric Orthogonal Polynomials and its q-Analogue. Delft University of Technology, Faculty of Information Technology and Systems, Department of Technical Mathematics and Informatics. Report 98-17 (1998)Google Scholar
  11. 11.
    Lindeberg, T., Fagerström, D.: Scale-Space with Causal Time Direction. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 229–240. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  12. 12.
    Martens, J.-B.: The Hermite Transform – Theory. IEEE Trans. Acoust. Speech Signal Processing 38(9), 1595–1606 (1990)MATHCrossRefGoogle Scholar
  13. 13.
    Palmer, S.E.: Vision Science. Photons to Phenomenology. The MIT Press, Cambridge (1999)Google Scholar
  14. 14.
    Porat, M., Zeevi, Y.Y.: The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision. IEEE Trans. Pattern Analysis Mach. Intell. 10, 452–468 (1988)MATHCrossRefGoogle Scholar
  15. 15.
    Puzicha, J., Hofmann, T., Buhmann, J.M.: Non-Parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 267–272 (1997)Google Scholar
  16. 16.
    Rivero-Moreno, C.J., Bres, S.: Conditions of Similarity between Hermite and Gabor Filters as Models of the Human Visual System. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 762–769. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Young, R.A., Lesperance, R.M., Meyer, W.W.: The Gaussian Derivative Model for Spatial-Temporal Vision: I. Cortical Model. Spatial Vision 14(3,4), 261–319 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Joel Rivero-Moreno
    • 1
  • Stéphane Bres
    • 1
  1. 1.LIRIS, UMR 5205 CNRS, Lab. d’InfoRmatique en Images et Systèmes d’information, INSA de LyonVilleurbanneFrance

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