Skip to main content
SpringerLink
Log in

Annotation, Storage, Retrieval and Analysis of Digital Video

  • Chapter
Book cover Intelligent Integrated Media Communication Techniques

  • 116 Accesses

Abstract

The volume of multimedia data that is generated everyday motivates a growing need for efficient and effective methods to index, organize, retrieve, and analyze video data. This chapter investigates techniques that are needed to access video data effectively. These techniques can be classified into three categories: frame-grouping, semantic association, and story/structure construction. To demonstrate our method of grouping we introduce a video query system based on similarity measures of low-level video features. We then demonstrate how predictive models can be built on the low-level video features in order to predict mid-level semantic characteristics associated with individual video units. Finally, by adopting syntactic pattern analysis, we show how high-level complex video patterns can be analyzed and syntactic models can be built to represent high-level video events that capture the structure of the video. We argue that all levels of video information-video meta-data-needs to be organized so that it is convenient for subsequent data storage, exchange, transmission, and analysis. As in the case with MPEG-7, we describe the use of an XML-based video meta-data for these purposes.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://www.google.com/, Jan. 2003.

    Google Scholar 

  2. http://www.yahoo.com/, Jan. 2003.

    Google Scholar 

  3. Y. Rui, T. S. Huang, and S. Mehrotra, “Constructing table-of-content for videos,” Multimedia Systems, vol. 7, no. 5, pp. 359–368, 1999.

    Article  Google Scholar 

  4. F. Idris and S. Panchanathan, “Review of image and video indexing techniques,” Journal of Visual Communication and Image Representation, vol. 8, pp. 146–166, June 1997.

    Google Scholar 

  5. G. Ahanger and T. D. C. Little, “A survey of technologies for parsing and indexing digital video,” Journal of Visual Communication and Image Representation, vol. 7, pp. 28–3, March 1996.

    Google Scholar 

  6. A. D. Bimbo, “Image and video database: visual browsing, querying and retrieval,” Journal of Visual Language and computing, vol. 7, pp. 353–359, December 1996.

    Google Scholar 

  7. R. Brunelli, O. Mich, and C. M. Modena, “A survey on the automatic indexing of video data,” Journal of Visual Communication and Image Representation, vol. 10, pp. 78–112, June 1999.

    Google Scholar 

  8. M. Flickner et al., “Query by image and video content: the QBIC system,” IEEE Computer Magazine, vol. 28, pp. 23–32, September 1995.

    Google Scholar 

  9. A. Hampapur and R. Jain, “Virage video engine,” in Proceedings of the SPIE 1998 Conference on Storage and Retrieval for Image and Video Databases V (R. C. J. Ishwar K. Sethi, ed.), vol. 3022, (San Jose, CA), pp. 188–197, February 1997.

    Google Scholar 

  10. S. F. Chang et al., “A fully automated content-based video search engine supporting spatiotemporal queries,” IEEE Transactions on Circuits and System for Video Technology, vol. 8, pp. 602–615, September 1998.

    Google Scholar 

  11. B. Gunsel, A. M. Tekalp, and P. J. L. van Beek, “Content-based access to video objects: temporal segmentation, visual summarization, and feature extraction,” Signal Processing, vol. 66, pp. 261–280, April 1998.

    Google Scholar 

  12. M. Carrer, L. Ligresti, G. Ahanger, and T. D. C. Little, “An annotation engine for supporting video database population,” Multimedia Tools and Techniques, vol. 5, pp. 233–258, November 1997.

    Google Scholar 

  13. F. Pereira and R. H. Koenen, Multimedia Systems, Standards, and Networks, ch. MPEG-7: status and directions. New York: Marcel Dekker, Inc., March 2000.

    Google Scholar 

  14. J. M. Martinez, “Overview of the MPEG-7 standard.,” Tech. Rep. N4509, ISO/IEC JTC1/SC29/WG11, December 2001.

    Google Scholar 

  15. N. Haering, R. J. Qian, and M. I. Sezan, “A semantic event-detection approach and its application to detecting hunts in wildlife video,” IEEE Transactions on Circuits and System for Video Technology, vol. 10, pp. 857–868, September 2000.

    Google Scholar 

  16. Y. A. Ivanov and A. F. Bobick, “Recognition of visual activities and interactions by stochastic parsing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 852–872, August 2000.

    Google Scholar 

  17. M. M. Yeung and B. L. Yeo, “Video content characterization and compaction for digital library applications,” in Proceedings of the SPIE 1998 Conference on Storage and Retrieval for Images and Video Databases V (R. C. J. Ishwar K. Sethi, ed.), vol. 3022, (San Jose, CA), pp. 45–58, February 1997.

    Google Scholar 

  18. R. Kasturi and R. Jain, Computer Vision: Principles, ch. Dynamic vision, pp. 469–480. Los Alamitos, CA: IEEE Computer Society Press, 1990.

    Google Scholar 

  19. A. Nagasaka and Y. Tanaka, “Automatic video indexing and full video search for object appearance,” in Proc. of the IFIP: Visual Database Systems II, pp. 113–127, 1992.

    Google Scholar 

  20. U. Gargi et al., “Evaluation of video sequence indexing and hierarchical video indexing,” in Proceedings of SPIE 1995 Conference on Storage and Retrieval for Images and Video Databases III (W. Niblack and R. C. Jain, eds.), vol. 2420, pp. 144–151, 1995.

    Google Scholar 

  21. Y. Tonomura, “Video handling based on structured information for hypermedia systems,” in Proc.ACM Int. Conf. Multimedia Information Systems, (Singapore), pp. 333–344, 1991.

    Google Scholar 

  22. S. Shahraray, “Scene change detection and content-based sampling of video sequences,” in Digital Video Compression: Algorithms Tech. 2419, pp. 2–13, 1995.

    Google Scholar 

  23. D. Swanberg, C. F. Shu, and R. Jain, “Knowledge guided parsing in video databases,” in Proceedings of SPIE 1993 Conference Storage and Retrieval for Images and Video Databases (W. Niblack, ed.), vol. 1908, pp. 13–24, 1993.

    Google Scholar 

  24. H. J. Zhang et al., “Automatic partitioning of full motion video,” ACM Multimedia Systems, vol. 1, no. 1, pp. 10–28, 1993.

    Google Scholar 

  25. A. Hampapur, R. Jain, and T. Weymouth, “Production model based digital video segmentation,” Multimedia Tools and Applications, vol. 1, pp. 9–46, Mar. 1995.

    Google Scholar 

  26. P. Aigrain and P. Joly, “The automatic real-time analysis of film editing and transition effects and its applications,” Comput. Graphics, vol. 18, no. 1, pp. 93–103, 1994.

    Article  Google Scholar 

  27. T. D. C. Little et al., “A digital on-demand video service supporting content-based queries,” in First ACM International Conference on Multimedia, (Anaheim, CA, USA), pp. 427–436, 1993.

    Google Scholar 

  28. H. J. Zhang et al., “Video parsing and browsing using compressed data,” Multimedia and Tools Applications, vol. 1, pp. 89–111, 1995.

    Google Scholar 

  29. H. J. Zhang et al., “Video parsing compressed data,” in SPIE: Image Video Processing II, (San Jose, CA, USA), pp. 142–149, 1994.

    Google Scholar 

  30. F. Arman, A. Hsu, and M. Y. Chiu, “Image processing on compressed data for large video databases,” in First ACM International Conference on Multimedia, (Anaheim, CA, USA), pp. 267–272, 1993.

    Google Scholar 

  31. F. Arman, A. Hsu, and M. Y. Chiu, “Feature management for large video databases,” in Storage and Retrieval for Images and Video Databases (W. Niblack, ed.), vol. 1908, pp. 2–12, 1993.

    Google Scholar 

  32. H. C. H. Liuand and G. L. Zick, “Scene decomposition of MPEG compressed video,” in Digital Video Compression: Algorithms Tech, (San Jose, CA, USA), pp. 26–37, 1995.

    Google Scholar 

  33. J. Lee and B. W. Dickinson, “Multiresolution video indexing for subband coded video databases,” in Image Video Processing II, (San Jose, CA, USA), pp. 321–330, 1994.

    Google Scholar 

  34. D. Santa-Cruz and T. Ebrahimi, “An analytical study of JPEG 2000 functionalities,” in Proc. of the International Conference on Image Processing, vol. 2, (Vancouver, Canada), pp. 49–52, 2000.

    Google Scholar 

  35. R. Lienhart, W. Effelsberg, and R. Jain, “VisualGREP: A systematic method to compare and retrieve video sequences,” Multimedia Tools and Applications, vol. 10, no. 1, pp. 47–72, 2000.

    Article  Google Scholar 

  36. M. Strieker and M, Orengo, “Similarity of color images,” in Storage and Retrieval for Images and Video Databases III (W. Remesh and C. Jain, eds.), vol. 2420, pp. 381–393, 1995.

    Google Scholar 

  37. Y. Gong et al., “An image database system with content capturing and fast image indexing abilities,” in Proceedings of the International Conference on Multimedia Computing and Systems, (Boston, MA, USA), pp. 121–130, 1994.

    Google Scholar 

  38. M. Tuceryan and A. K. Jain, Handbook of Pattern Recognition and Computer Vision, ch. Texture analysis, pp. 235–276. World Scientific Publishing, 1993.

    Google Scholar 

  39. G. Taubin and D. B. Coper, “Recognition and positioning of rigid objects using algebraic moment invariants,” in Geometric Methods Computer Vision (B. C. Vemuri, ed.), vol. 1570, pp. 175–186, 1991.

    Google Scholar 

  40. E. Persoon and K. S. Fu, “Shape discrimination using fourier descriptors,” IEEE Trans. Systems Man Cybernetics, vol. 8, pp. 170–179, 1977.

    MathSciNet  Google Scholar 

  41. S. R. Rubois and F. H. Glanz, “An autoregressive model approach to dimensional shape classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 55–66, 1986.

    Google Scholar 

  42. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1989.

    Google Scholar 

  43. A. K. Jain, A. Vailaya, and X. Wei, “Query by video clip,” Multimedia Systems, vol. 7, pp. 369–384, 1999.

    Article  Google Scholar 

  44. A. Akutsu et al., “Video indexing using motion vectors,” in Visual Communications and Image Processing’92 (P. Maragos, ed.), vol.1818, pp. 1522–1530, 1992.

    Google Scholar 

  45. H. Ueda, T. Miyataka, and S. Yoshizawa, “IMPACT: An interactive natural-motion-picture dedicated multimedia authoring system,” in Proc. Human Factors in Computing Systems CHI’91, (New Orleans, Louisiana, USA), pp. 343–350, 1991.

    Google Scholar 

  46. W. Wolf, “Key frame selection by motion analysis,” in Proceeding IEEE Int. Conf. Acoust., Speech and Signal Proc., (Atlanta, GA, USA), pp. 1228–1231, 1996.

    Google Scholar 

  47. X. Liu, C. B. Owen, and F. Makedon, “Automatic video pause detection filter,” Tech. Rep. PCS-TR97-307, Dartmouth College, Computer Science, Hanover, NH, USA, February 1997.

    Google Scholar 

  48. M. M. Yeung and B. Liu, “Efficient matching and clustering of video shots,” in Proceedings of the IEEE Intl. Conference on Image Processing, vol. 1, (Washington, D.C.), pp. 338–341, Oct. 1995.

    Google Scholar 

  49. W. Xiong, J. C. M. Lee, and R. H. Ma, “Automatic video data structuring through shot partitioning and key-frame computing,” Machine Vision and Applications, vol. 10, pp. 51–65, 1997.

    Article  Google Scholar 

  50. http://www.w3.org/XML, Jan. 2003.

    Google Scholar 

  51. H. Jiang and A. K. Elmagarmid, “WVTDB-a semantic content-based video database system on the world wide web,” IEEE Transactions on Knowledge and Data Engineering, vol. 10, no. 6, pp. 947–966, 1998.

    Article  Google Scholar 

  52. T. Kawashima et al., “Indexing of baseball telecast for content-based video retrieval,” in Proceedings of International Conference on Image Processing, (Chicago, IL, USA), pp. 871–874, 1998.

    Google Scholar 

  53. T. Kato et al., “A sketch retrieval method for full color image database query by visual example,” in Proceedings of 11th IAPR International Conference on Pattern Recognition, (The Hague, The Netherlands), pp. 530–533, 1992.

    Google Scholar 

  54. E. Ardizzone et al., “Motion and color based video indexing and retrieval,” in Proc. Int. Conf. on Pattern Recognition (ICPR-96), vol. III, (Vienna, Austria), pp. 135–139, 1996.

    Google Scholar 

  55. T. M. Cover and J. Thomas, Elements of information theory. John Wiley & Sons, Inc., 1991.

    Google Scholar 

  56. H. Li and Y. Zhao. http://eepc269.eng.ohio-state.edu/matlab/, Jan. 2003.

  57. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer Verlag, 1995.

    Google Scholar 

  58. B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, (Pittsbugh, PA, USA), pp. 144–152, ACM Press, 1992.

    Google Scholar 

  59. V. Vapnik and A. Chervonenkis, Theory of Pattern Recognition [in Russian]. Nauka, Moscow, 1974.

    Google Scholar 

  60. B. Scholkopf, C. Burges, and V. Vapnik, “Extracting support data for a given task,” in Proceedings 1st International Conference on Knowledge Discovery & Data Mining, (Menlo Park, CA), pp. 252–257, 1995.

    Google Scholar 

  61. K. S. Fu, Syntactic Pattern Recognition and Applications. Englewood Cliffs: Prentice-Hall, 1982.

    Google Scholar 

  62. T. Kohonen, Self-Organizing Maps. Spinger-Verlag, 1995.

    Google Scholar 

  63. S. C. Ahalt et al., “Competitive learning algorithms for vector quantization,” Neural Networks, vol. 3, pp. 277–290, May 1990.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, The Ohio State University, Columbus, Ohio, 43210, USA

    Honglin Li, Yi Zhao, José-Luis Sancho-Gómez & Stanley Ahalt

Authors
  1. Honglin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José-Luis Sancho-Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stanley Ahalt
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Ljubljana, Slovenia

    Jurij F. Tasič

  2. University of Bordeaux I, France

    Mohamed Najim

  3. University of Neuchâtel, Switzerland

    Michael Ansorge

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Kluwer Academic Publishers

About this chapter

Cite this chapter

Li, H., Zhao, Y., Sancho-Gómez, JL., Ahalt, S. (2003). Annotation, Storage, Retrieval and Analysis of Digital Video. In: Tasič, J.F., Najim, M., Ansorge, M. (eds) Intelligent Integrated Media Communication Techniques. Springer, Boston, MA. https://doi.org/10.1007/0-306-48718-7_2

Download citation

  • DOI: https://doi.org/10.1007/0-306-48718-7_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7552-0

  • Online ISBN: 978-0-306-48718-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation