Video Summarisation for Surveillance and News Domain

  • Uros Damnjanovic
  • Tomas Piatrik
  • Divna Djordjevic
  • Ebroul Izquierdo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4816)


Video summarization approaches have various fields of application, specifically related to organizing, browsing and accessing large video databases. In this paper we propose and evaluate two novel approaches for video summarization, one based on spectral methods and the other on ant-tree clustering. The overall summary creation process is broke down in two steps: detection of similar scenes and extraction of the most representative ones. While clustering approaches are used for scene segmentation, the post-processing logic merges video scenes into a subset of user relevant scenes. In the case of the spectral approach, representative scenes are extracted following the logic that important parts of the video are related with high motion activity of segments within scenes. In the alternative approach we estimate a subset of relevant video scene using ant-tree optimization approaches and in a supervised scenario certain scenes of no interest to the user are recognized and excluded from the summary. An experimental evaluation validating the feasibility and the robustness of these approaches is presented.


Spectral clustering ant-tree clustering scene detection video summarization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Calic, J., Izquierdo, E.: Towards real time shot detection in the MPEG compressed domain. In: Proceedings of the Workshop on Image Analysis for Multimedia Interactive Services (2001)Google Scholar
  2. 2.
    Yeo, L.B., Liu, B.: Rapid scene analysis on compressed video. IEEE Transactions on Circuits & Systems for Video Technology 5, 533–544 (1995)CrossRefGoogle Scholar
  3. 3.
    Lee, J., Lee, G.G., Kim, W.Y.: Automatic video summarizing tool using MPEG-7 descriptors for personal video recorder. IEEE Transaction on Consumer Electronics 49, 742–749 (2003)CrossRefGoogle Scholar
  4. 4.
    Rasheed, Z., Shan, M.: Detection and Representation of scenes in videos. IEEE Transactions on Multimedia 7, 1097–1105 (2005)CrossRefGoogle Scholar
  5. 5.
    Odobez, J., Gatica-Perez, D., Guillemot, M.: Video shot clustering using spectral methods. In: 3rd Workshop on Content Based Multimedia Indexing (CBMI) (2003)Google Scholar
  6. 6.
    Li, Z., Schuster, G.M., Katsaggelos, A.K.: MINMAX optimal video summarization. IEEE Transactions on Circuits and Systems for Video Technology 15, 1245–1256 (2005)CrossRefGoogle Scholar
  7. 7.
    Osuka, I., Radharkishnan, R., Siracusa, M., Divakaran, A., Mishima, H.: An enhanced video summarization system using audio features for personal video recorder. IEEE Transactions on Consumer Electronics 52, 168–172 (2006)Google Scholar
  8. 8.
    Wang, Y., Zhang, T., Tretter, D.: Real time motion analysis towards semantic understanding of video content. In: Conference on Visual Communications and Image Processing (2005)Google Scholar
  9. 9.
    Peker, K.A., Alatan, A.A., Akansu, A.N.: Low level motion activity features for semantic characterization of video. IEEE International Conference on Multimedia and Expo 2, 801–804 (2000)Google Scholar
  10. 10.
    Peyrard, N., Bouthemy, P.: Motion-based selection of relevant video segments for video summarization. Multimedia Tools and Applications, pp. 259-276 (2005)Google Scholar
  11. 11.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on PAMI 22, 888–905 (2000)Google Scholar
  12. 12.
    Alpert, C., Khang, A., Yao, S.: Spectral partitioning: The more eigenvectors, the better. Discrete Applied Mathematics (1999)Google Scholar
  13. 13.
    Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG-7. John Willey & Sons, New York, NY (2002)Google Scholar
  14. 14.
    Zheng, X., Lin, X.: Automatic determination of intrinsic cluster number family in spectral clustering using random walk on graph. In: ICIP 2004. International Conference on Image Processing, vol. 5, pp. 3471–3474 (2004)Google Scholar
  15. 15.
    Meila, M., Shi, J.: A random walks view of spectral segmentation. AI and Statistic (2001)Google Scholar
  16. 16.
    Holldobler, B., Wilson, E.O.: The Ants. Springer, Heidelberg (1990)Google Scholar
  17. 17.
    Dorigo, M., Di Caro, G.: Ant Algorithms for Discrete Optimization. Technical Report, pp. 98–10 (1999) Google Scholar
  18. 18.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  19. 19.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artifical Systems. Oxford University Press, Oxford (1999)Google Scholar
  20. 20.
    Lumer, E., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: 3tl1 Conference on simulation and adaptive behavior-: from animals to animats, pp. 501–508 (1994)Google Scholar
  21. 21.
    Kuntz, P., Snyers, D., Layzell, P.: A stochastic heuristic for visualizing graph clusters in a hi-dimensional space prior to partitioning. Journal of Heuristic 5 (1999)Google Scholar
  22. 22.
    Labroche, N., Monmarche, N., Venturini, G.: A new clustering algorithm based on the chemical recognition system of ants. In: Proceedings of the 15th European Conference on Artifical Inteligence (2002)Google Scholar
  23. 23.
    Azzag, N., Monmarch, H., Slimane, M., Venturini, G., Guinot, C.: Antree: a new model for clustering with artificial ants. In: IEEE Congress on Evolutionary Computation, pp. 8–12 (2003)Google Scholar
  24. 24.
    Lioni, A., Sauwens, C., Theraulaz, G., Deneubourg, J.L.: The dynamics of chain formation in Oecophylia longinoda. Journal of Insect Behavior 14, 679–696 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Uros Damnjanovic
    • 1
  • Tomas Piatrik
    • 1
  • Divna Djordjevic
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Department of Electronic Engineering, Queen Mary, University of London, London E1 4NSU.K.

Personalised recommendations