A Video Summarization Method Based on Spectral Clustering

  • Marcos Vinicius Mussel Cirne
  • Helio Pedrini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

The constant increase in the availability of digital videos has demanded the development of techniques capable of managing these data in a faster and more efficient way, especially concerning the content analysis. One of the research areas that have recently evolved significantly at this point is video summarization, which consists of generating a short version of a certain video, such that the users can grasp the central message transmitted by the original video. Many of the video summarization approaches make use of clustering algorithms, with the goal of extracting the most important frames of the videos to compose the final summary. However, special clustering algorithms based on a spectral approach have obtained superior results than those obtained with classical clustering algorithms, not only in video summarization techniques but also in other fields, such as machine learning, pattern recognition, and data mining. This work proposes a method for summarization of videos, regardless of their genre, using spectral clustering algorithms. Possibilities of algorithm parallelization for the purpose of optimizing the general performance of the proposed methodology are also discussed.

Keywords

Spectral Cluster Original Video Video Shot Video Summarization Shot Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    OpenCV: Open Source Computer Vision (2013), http://opencv.org
  2. 2.
    The Open Video Project (2013), http://www.open-video.org
  3. 3.
    VSUMM (Video SUMMarization) (2013), https://sites.google.com/site/vsummsite
  4. 4.
    de Avila, S.E.F., Lopes, A.P.B., da Luz Jr., A., de Albuquerque Araújo, A.: VSUMM: A Mechanism Designed to Produce Static Video Summaries and a Novel Evaluation Method. Pattern Recognition Letters 32(1), 56–68 (2011)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Chasanis, V., Likas, A., Galatsanos, N.: Video Rushes Summarization Using Spectral Clustering and Sequence Alignment. In: 2nd ACM TRECVid Video Summarization Workshop, Vancouver, BC, Canada, pp. 75–79 (2008)Google Scholar
  8. 8.
    Damnjanovic, U., Izquierdo, E., Grzegorzek, M.: Shot Boundary Detection Using Spectral Clustering. In: 15th European Signal Processing Conference, Poznan, Poland, pp. 1779–1783 (September 2007)Google Scholar
  9. 9.
    Elhamifar, E., Sapiro, G., Vidal, R.: See All by Looking at a Few: Sparse Modeling for Finding Representative Objects. In: IEEE Computer Vision and Pattern Recognition, Los Alamitos, CA, USA, pp. 1600–1607 (2012)Google Scholar
  10. 10.
    Furini, M., Geraci, F., Montangero, M., Pellegrini, M.: STIMO: STIll and MOving Video Storyboard For The Web Scenario. In: Multimedia Tools and Applications, vol. 46, pp. 47–69. Kluwer Academic Publishers, Hingham (2010)Google Scholar
  11. 11.
    Guimarães, S.J.F., Couprie, M., Araújo, A.D.A., Leite, N.J.: Video Segmentation Based on 2D Image Analysis. Pattern Recognition Letters 24(7), 947–957 (2003)CrossRefGoogle Scholar
  12. 12.
    Lowe, D.: Object Recognition from Local Scale-Invariant Features. In: Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Luxburg, U.: A Tutorial on Spectral Clustering. Statistics and Computing 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Money, A.G., Agius, H.: Video Summarisation: A Conceptual Framework and Survey of the State of the Art. Journal of Visual Communication and Image Representation 19(2), 121–143 (2008)CrossRefGoogle Scholar
  15. 15.
    Mundur, P., Rao, Y., Yesha, Y.: Keyframe-Based Video Summarization Using Delaunay Clustering. International Journal on Digital Libraries 6, 219–232 (2006)CrossRefGoogle Scholar
  16. 16.
    Peng, J., Xiaolin, Q.: Keyframe-Based Video Summary Using Visual Attention Clues. IEEE MultiMedia 17(2), 64–73 (2010)Google Scholar
  17. 17.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An Efficient Alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, Barcelona, Spain (2011)Google Scholar
  18. 18.
    Sanguinetti, G., Laidler, J., Lawrence, N.D.: Automatic Determination of the Number of Clusters Using Spectral Algorithms. In: IEEE Machine Learning for Signal Processing, pp. 28–30 (2005)Google Scholar
  19. 19.
    Shekar, B., Raghurama Holla, K., Sharmila Kumari, M.: Video Shot Detection Using Cumulative Colour Histogram. In: Mohan, S., Kumar, S.S. (eds.) 4th International Conference on Signal and Image Processing. LNEE, vol. 222, pp. 353–363. Springer, Heidelberg (2012)Google Scholar
  20. 20.
    Truong, B.T., Venkatesh, S.: Video Abstraction: A Systematic Review and Classification. ACM Transactions on Multimedia Computing, Communications and Applications 3(1) (February 2007)Google Scholar
  21. 21.
    Zhou, H., Sadka, A.H., Swash, M.R., Azizi, J., Sadiq, U.A.: Feature Extraction and Clustering for Dynamic Video Summarisation. Neurocomputing 73, 1718–1729 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marcos Vinicius Mussel Cirne
    • 1
  • Helio Pedrini
    • 1
  1. 1.Institute of ComputingUniversity of CampinasCampinasBrazil

Personalised recommendations