Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Video sequence similarity matching

  • Video Retrieval
  • Conference paper
  • First Online:
Multimedia Information Analysis and Retrieval (MINAR 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1464))

Abstract

Content-based retrieval of multimedia data with temporal constraints, such as video and audio sequences, requires a consideration of the temporal ordering inherent in such sequences. Video sequence-tosequence matching is therefore an important step in realizing content-based video retrieval. This paper provides an overview of the general issues involved in video sequence matching, points out the immediate problems that must be addressed before video sequence matching becomes practical, and proposes some general methods to address the problems. Implications of the video sequence matching problem on other areas of multimedia information retrieval are also highlighted.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adjeroh D.A., Lee M.C. and 1. King, “A distance measure for video sequence similarity matching”, Proc., IW-MMDBMS'98, Ohio, August 1998, to appear.

    Google Scholar 

  2. Adjeroh, D. A. and Lee, M.C., “Adaptive Transform Domain Video Scene Analysis”, Proc., IEEE Multimedia97, Ottawa Canada, June 3–6, 1997.

    Google Scholar 

  3. Burt P. and Adelson E., The Laplacian pyramid as a compact code”, IEEE Trans. on Communications, 31, 523–540. 1983.

    Google Scholar 

  4. Chang C-W, and Lee S-Y, “Video content representation; indexing, and matching in video information systems”, J. Visual Comm. & Image Repr., 8, 2, 107–120. 1997.

    Google Scholar 

  5. Chang W.I. and Lawler E.L., “Sublinear approximate string matching and biological applications”. Algorithmica, 12:327–344, 1994.

    Google Scholar 

  6. Cheever E.A., et al, “Using signal processing techniques for DNA sequence comparison”, Proc., 5th Annual Northeast Bioengineering Conf., pp. 173–174, MA, 1989.

    Google Scholar 

  7. Dimitrova N. and Golshani F., “Motion recovery for video content classification”, ACM Trans. on Information Systems, 13, 4, 408–439, 1995.

    Google Scholar 

  8. Faloutsos C, Ranganathan M, and Manolopoulos Y, “Fast subsequence matching in time-series databases”, Proc., ACM SIGMOD, pp. 419–429, Minneapolis MN, 1994.

    Google Scholar 

  9. Galil Z and Park K., “An improved algorithm for approximate string matching”, SIAM J. of Computing, 19, 6, 989–999, 1990.

    Google Scholar 

  10. Gonzalez R.C. and Woods R.E., Digital Image Processing. Addison-Wesley Publishing Co., Readings Massachusetts, 1992.

    Google Scholar 

  11. Idris F. and Panchanathan S., “Review of image and video indexing”, J. Visual Comm. & and Image Repr., 8,2,146–166, 1997.

    Google Scholar 

  12. Iorka M. and Masato Kurokawa, “Estimation of motion vectors and their aoolication to scene retrieval”, Machine Vision and Applications, 7:199–208, 1994.

    Google Scholar 

  13. Irani M. and Anandan P., “Video indexing based on mosaic representations”, Tech. Report, Dept. of Applied Maths. & Comp. Sc., The Weizmann Institute, Israel, 1997.

    Google Scholar 

  14. Lee J-S, et al, “Efficient algorithms for approximate string matching with swaps”, LNCS 1264, Combinatorial Pattern Matching, pp. 28–39, 1997.

    Google Scholar 

  15. Milosavljevic A., “Discovering dependencies via algorithmic mutual information: a case study in DNA sequence comparison”, Machine Learning, 21, 35–50, 1995.

    Google Scholar 

  16. Myers E.W., “A sublinear algorithm for approximate keyword searching”, Algorithmica, 12: 345–374, 1994

    Google Scholar 

  17. Ohya M, “Information treatment of genes”, Trans., Japanese IEICE, E72 5, 556–560, 1989.

    Google Scholar 

  18. Pevzner P.A. and M.S. Waterman, “A fast filteration algorithm for the substring matching problem”, LNCS 684, Combinatorial Pattern Matching, pp. 197–214, 1993.

    Google Scholar 

  19. Sankoff D., “Edit distance for genome comparison based on non-local operations”, LNCS 644, Combinatorial Pattern Matching, pp. 121–135, 1992.

    Google Scholar 

  20. Sawhney H.S., and Ayer S., “Compact representation of videos through dominant and multiple motion estimation”, IEEE Trans. PAMI, 18, 8, 814–830, 1996.

    Google Scholar 

  21. Sellers P.H, “The theroy of computation of evolutionary distances: pattern ecognition”, J. of Algorithms, 1, 359–373, 1980.

    Google Scholar 

  22. Tai K.C., “The tree-to-tree correction problem”, J. of ACM, 26: 422–433, 1979.

    Google Scholar 

  23. Ukkonen E., “Finding approximate patterns in strings”, J. of Algorithms, 6, 132–137, 1985.

    Google Scholar 

  24. Wagner A. and Fischer M.J, “The string-to-string correction problem”, J. of ACM, 21: 168–173, 1974.

    Google Scholar 

  25. Waterman, M.S. (ed.), Mathematical Methods for DNA Sequences, CRC Press, Boca Raton, Florida, 1989.

    Google Scholar 

  26. Wendling F.,et al, “Extraction of spatio temporal signatures from depth EEG seizure signals based on objective matching in warped vectorial observations”, IEEE Trans. Biomedical Engineering, 43, 10, 990–1000, 1996.

    Google Scholar 

  27. Wold W., et al, “Content-based classification, search and retrieval of audio”, IEEE Multimedia, 3, 3, 27–36, 1996.

    Google Scholar 

  28. Wooton J.C. and Federhen S., “Statistics of local complexity in amino acid sequences and sequence databases”, Computers and Chemistry, 17, 2, 149–163, 1993.

    Google Scholar 

  29. Yazdani N and Ozsoyoglu Z.M., “Sequence matching of image”, Proc., 8th Int'l Conf. on Scientific and Statistical Database Management, pp. 53–62, 1996.

    Google Scholar 

  30. Zhang K, and Shasha D., “Simple fast algorithms for the editing distance between trees and related problems”, SIAM J. of Computing, 18, 6, 1245–1252, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Horace H. S. Ip Arnold W. M. Smeulders

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Adjeroh, D.A., King, I., Lee, M.C. (1998). Video sequence similarity matching. In: Ip, H.H.S., Smeulders, A.W.M. (eds) Multimedia Information Analysis and Retrieval. MINAR 1998. Lecture Notes in Computer Science, vol 1464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0016490

Download citation

  • DOI: https://doi.org/10.1007/BFb0016490

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64826-0

  • Online ISBN: 978-3-540-68537-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics