Skip to main content
SpringerLink
Log in

Automatic Video Database Indexing and Retrieval

  • Chapter
Representation and Retrieval of Video Data in Multimedia Systems

Abstract

The increasing development of advanced multimedia applications requires new technologies for organizing and retrieving by content databases of still digital images or digital video sequences. To this aim image and image sequence contents must be described and adequately coded. In this paper we describe a system allowing content-based annotation and querying in video databases. No user action is required during the database population step. The system automatically splits a video into a sequence of shots, extracts a few representative frames (said r-frames) from each shot and computes r-frame descriptors based on color, texture and motion. Queries based on one or more features are possible. Very interesting results obtained during the severe tests the system was subjected to are reported and discussed.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. P. Aigrain and P. Joly, “Medium knowledge-based macro-segmentation of video into sequences,” in Intelligent Multimedia Information Retrieval, M. Maybury (Ed.), 1996.

    Google Scholar 

  2. E. Ardizzone and M. La Cascia, “Motion labeling in video databases,” Journal of Visual Languages and Computing: Special Issue on Image and Video Database Visual Browsing Querying and Retrieval, 1996, (submitted).

    Google Scholar 

  3. E. Ardizzone, A. Chella, and S. Gaglio, “A hybrid architecture for shape reconstruction and object recognition,” in International Journal of Intelligent Systems, 1996, (in press).

    Google Scholar 

  4. E. Ardizzone, G.A.M. Gioiello, M. La Cascia, and D. Molinelli, “A real-time neural approach to scene cut detection,” in Proc. of IS&T/SPIE—Storage & Retrieval for Image and Video Databases IV, San Jose, 1996.

    Google Scholar 

  5. E. Ardizzone, M. La Cascia, and D. Molinelli, “Motion and color based video indexing and retrieval,” in Proc. of Int. Conf. on Pattern Recognition, ICPR, Wien, Austria, 1996.

    Google Scholar 

  6. F. Arman, A. Hsu, and M.Y. Chiu, “Image processing on compressed data for large video databases,” in Proc. First ACM Int. Conf. Multimedia, 1993.

    Google Scholar 

  7. J.R. Bach, S. Paul, and R. Jain, “A visual information management system for the interactive retrieval of faces,” IEEE Transaction on Knowledge and Data Engineering, Vol. 5, 1993.

    Google Scholar 

  8. J.L. Barron, D.J. Fleet, and S.S. Beauchemin, “Performances of optical flow techniques,” Int. Journal of Computer Vision, Vol. 12, p. 43, 1994.

    Article  Google Scholar 

  9. M.G. Christel, “Addressing the contents of video in a digital library,” in Proc. of ACM Workshop on Effective Abstractions in Multimedia: Layout, Presentation and Interaction, San Francisco, 1995.

    Google Scholar 

  10. J.M. Corridoni and A. DelBimbo, “Automatic video segmentation through editing analysis,” in Proc. of 8th Int. Conf. on Image Analysis and Processing, ICIAP, Sanremo, Italy, 1995.

    Google Scholar 

  11. J.M. Corridoni and A. DelBimbo, “Film semantic analysis,” in Proc. of Computer Architecture for Machine Perception, CAMP, Como, Italy, 1995.

    Google Scholar 

  12. J.M. Corridoni, A. DelBimbo, and D. Lucarella, “Navigation and visualization of movies content,” in Proc. Int. Conf. on Visual Languages, VL’95, Darmstadt, Germany, 1995.

    Google Scholar 

  13. C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Pektovic, and W. Equitz, “Efficient and effective querying by image content,” Journal of Intelligent Information Systems, Vol. 3, p. 231, 1994.

    Article  Google Scholar 

  14. M. Flickner et al. “Query by image and video content: The qbic system,” IEEE Computer, Sept. 1995.

    Google Scholar 

  15. W.I. Grosky, “Multimedia information systems,” IEEE Multimedia, Vol. 2, 1994.

    Google Scholar 

  16. V.N. Guditava and V.V. Raghavan, “Content-based image retrieval systems,” IEEE Computer, Sept. 1995.

    Google Scholar 

  17. A. Hampapur, T. Weymouth, and R. Jain, “Digital video segmentation,” in ACM Multimedia’94 Proceedings, ACM Press, 1994.

    Google Scholar 

  18. A. Hampapur, R. Jain, and T. Weymouth, “Production model based digital video segmentation,” Journal of Multimedia Tools and Applications, Vol. 1,No. 1, pp. 9–46, March 1995.

    Article  Google Scholar 

  19. B.K.P. Horn and B.G. Schunk, “Determining optical flow,” Artificial Intelligence, p. 17, 1981.

    Google Scholar 

  20. R.W.G. Hunt, Measuring Color, John Wiley & Sons, 1989.

    Google Scholar 

  21. P.M. Kelly and T.M. Cannon, “Candid: Comparison algorithm for navigating digital image databases,” in Proc. of the Seventh International Working Conference on Scientific and Statistical Database Management, Charlottesville VA, 1994.

    Google Scholar 

  22. P.M. Kelly, T.M. Cannon, and D.R. Hush, “Query by image example: The candid approach,” in Proc. of SPIE: Storage and Retrieval for Image and Video Database III, 1995.

    Google Scholar 

  23. M. La Cascia and E. Ardizzone, “Jacob: Just a content-based query system for video databases,” in Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP-96, Atlanta, May, 1996, pp. 7–10.

    Google Scholar 

  24. D. Lee, R. Barber, W. Niblack, M. Flickner, J. Hafner, and D. Petkovic, “Indexing for complex queries on a query by content image database,” in Proceedings of the ICPR, Vol. 1, pp. 142–146, 1994.

    Google Scholar 

  25. D. Lee, R. Barber, W. Niblack, M. Flickner, J. Hafner, and D. Petkovic, “Query by image content using multiple objects and multiple features: User interface issue,” in Proceeding of the ICIP, 1994.

    Google Scholar 

  26. W. Li, S. Gauch, J. Gauch, and K.M. Pua, “Vision: A digital video library,” in ACM Digital Libraries, Bethseda, MD, 1996.

    Google Scholar 

  27. F. Liu and R.W. Picard, “Periodocity, directionality and randomness,” Technical Report TR-320, MIT Media Lab.

    Google Scholar 

  28. T.P. Minka and R.W. Picard, “Interactive learning using a society of models,” Technical Report TR-349, MIT Media Lab.

    Google Scholar 

  29. A. Nagasaka and Y. Tanaka, “Automatic video indexing and full-motion search for object appearances,” in Proc. IFIP TC2/WG2.6 Second Working Conference on Visual Database System, 1991.

    Google Scholar 

  30. H.H. Nagel, “Displacement vectors derived from second-orderintensity variations in image sequences,” Computer Vision, Graphics, Image Processing, Vol. 21, 1983.

    Google Scholar 

  31. H.H. Nagel, “On the estimation of optical flow: Relations between different approaches and some new results,” Artificial Intelligence, Vol. 33, 1987.

    Google Scholar 

  32. W. Niblack et al., “The qbic project: Querying images by content using color, texture and shape,” in IS&T/SPIE Symposium on Electronic Imaging: Science and Technology—Storage & Retrieval for Image and Video Databases I, San Jose, CA, 1993.

    Google Scholar 

  33. V.E. Ogle and M. Stonebraker, “Chabot: Retrieval from a relational database of images,” IEEE Computer, Sept. 1995.

    Google Scholar 

  34. E. Oomoto and K. Tanaka, “Ovid: Design and implementation of a video-object database system,” IEEE Transaction on Knowledge and Data Engineering, Vol. 5, Aug. 1994.

    Google Scholar 

  35. A. Pentland, R.W. Picard, and S. Sclaroff, “Photobook: Tools for content-based manipulation of image databases,” in Proc. of SPIE: Storage and Retrieval Image and Video Database II, San Jose, Feb. 6–10, 1994.

    Google Scholar 

  36. R.W. Picard, “Computer learning of subjectivity,” Technical Report TR-359, MIT Media Lab.

    Google Scholar 

  37. R.W. Picard, “Toward a visual thesaurus,” in Proc. of Springer Werlag Workshops in Computing, MIRO, Glasgow, 1995.

    Google Scholar 

  38. R.W. Picard and T. Kabir, “Finding similar patterns in large image databases,” in Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Minneapolis, MN, 1993.

    Google Scholar 

  39. R.W. Picard, T. Kabir, and F. Liu, “Real-time recognition with the entire broadatz texture database,” in Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, CVPR, New York, NY. 1993.

    Google Scholar 

  40. R.W. Picard and T.P. Minka, “Vision texture for annotation,” Technical Report TR-302, MIT Media Lab.

    Google Scholar 

  41. M.J.D. Powell, “Restart procedures for the conjugate gradient method,” Mathematical Programming, Vol. 12, pp. 241–254, 1968.

    Article  MathSciNet  Google Scholar 

  42. S. Sclaroff, “Deformable prototypes for encoding shape categories in image databases,” Pattern Recognition: Special issue on image databases, 1996, (to appear).

    Google Scholar 

  43. J.R. Smith and S.-F. Chang, “Querying by color regions using the visualseek content-based visual query system,” in Intelligent Multimedia Information Retrieval, M. Maybury (Ed.), 1996.

    Google Scholar 

  44. J.R. Smith and S.-F. Chang, “Tools and techniques for color image retrieval,” in Proc. of IS&T SPIE: Storage and Retrieval Image and Video Database IV, San Jose, CA, 1996.

    Google Scholar 

  45. R.K. Srihari, “Automatic indexing and content-based retrieval of captioned images,” IEEE Computer, Sept. 1995.

    Google Scholar 

  46. H.S. Stone and T.G. Shamoon, “The use of image content to control image retrieval and image processing,” Technical Report, Internal Report, NEC Research Institute, 1995.

    Google Scholar 

  47. M. Stricker and A. Dimai, “Color indexing with weak spatial constraints,” in Proc. of IS&T SPIE: Storage and Retrieval Image and Video Database IV, San Jose, CA, 1996.

    Google Scholar 

  48. M. Stricker and M. Orengo, “Similarity of color images,” in Proc. of IS&T SPIE: Storage and Retrieval Image and Video Database III, San Jose, CA, 1995.

    Google Scholar 

  49. M. Swain and D. Ballard, “Color indexing,” Int. Journal of Computer Vision, Vol. 7, p. 11, 1991.

    Article  Google Scholar 

  50. H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Transaction on Systems, Man and Cybernetics, Vol. 8,No. 6, 1978.

    Google Scholar 

  51. W. Wolf, “Key-frame selection by motion analysis,” in Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Atlanta, May 7–10, 1996.

    Google Scholar 

  52. B.L. Yeo and B. Liu, “Rapid scene analysis on compressed video,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 5, Dec. 1995.

    Google Scholar 

  53. M.M. Yeung and B. Liu, “Efficient matching and clustering of video shot,” in Proc. of IEEE Int. Conf. on Image Processing, ICIP, Washington, DC, 1995.

    Google Scholar 

  54. M.M. Yeung, B.L. Yeo, and B. Liu, “Extracting story units from long programs for video browsing and navigation,” in Proc. of Int. Conf. on Multimedia Computing and Systems, 1996.

    Google Scholar 

  55. M.M. Yeung, B.L. Yeo, W. Wolf, and B. Liu, “Video browsing using clustering and scene transitions on compressed sequences,” in Proc. of IS&T/SPIE Multimedia Computing and Networking, 1995.

    Google Scholar 

  56. A. Yoshitaka, S. Kishida, M. Hirakawa, and T. Ichikawa, “Knowledge-assisted content-based retrieval for multimedia databases,” IEEE Multimedia, Vol. 1, 1994.

    Google Scholar 

  57. H.J. Zhang, A. Kankanhalli, and S.W. Smoliar, “Automatic partitioning of full-motion video,” Multimedia Systems, Vol. 1, 1993.

    Google Scholar 

  58. H.J. Zhang, C.Y. Low, S.W. Smoliar, and J.H. Wu, “Video parsing retrieval and browsing: An integrated and content-based solution,” in Proc. ACM Multimedia’95, pp. 15–24.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Palermo, Palermo, Italy

    Edoardo Ardizzone & Marco La Cascia

Authors
  1. Edoardo Ardizzone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco La Cascia
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Hewlett Packard Laboratories, USA

    Hong Jiang Zhang

  2. Universite Paul Sabatier, France

    Philippe Aigrain

  3. IBM Almaden Research Center, USA

    Dragutin Petkovic

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Kluwer Academic Publishers

About this chapter

Cite this chapter

Ardizzone, E., La Cascia, M. (1997). Automatic Video Database Indexing and Retrieval. In: Zhang, H.J., Aigrain, P., Petkovic, D. (eds) Representation and Retrieval of Video Data in Multimedia Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-31786-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-0-585-31786-1_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-9863-9

  • Online ISBN: 978-0-585-31786-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation