STIMO: STIll and MOving video storyboard for the web scenario

  • Marco Furini
  • Filippo Geraci
  • Manuela Montangero
  • Marco Pellegrini


In the current Web scenario a video browsing tool that produces on-the-fly storyboards is more and more a need. Video summary techniques can be helpful but, due to their long processing time, they are usually unsuitable for on-the-fly usage. Therefore, it is common to produce storyboards in advance, penalizing users customization. The lack of customization is more and more critical, as users have different demands and might access the Web with several different networking and device technologies. In this paper we propose STIMO, a summarization technique designed to produce on-the-fly video storyboards. STIMO produces still and moving storyboards and allows advanced users customization (e.g., users can select the storyboard length and the maximum time they are willing to wait to get the storyboard). STIMO is based on a fast clustering algorithm that selects the most representative video contents using HSV frame color distribution. Experimental results show that STIMO produces storyboards with good quality and in a time that makes on-the-fly usage possible.


Video summary Video browsing Clustering Storyboards 


  1. 1.
    Bouch A, Kuchinsky A, Bhatti N (2000) Quality is in the eye of the beholder: meeting users’ requirements for internet quality of service. In: Proc. of conference on human factors in computing systems, The Hague, pp 297–304Google Scholar
  2. 2.
    Charikar MS (2002) Similarity estimation techniques from rounding algorithms. In: Proceedings of 34th annual ACM symposium on the theory of computing, Montreal, pp 380–388Google Scholar
  3. 3.
    Feder T, Greene D (1988) Optimal algorithms for approximate clustering. In: Proceedings of the 28th ACM symposium on theory of computing, pp 434–444Google Scholar
  4. 4.
    Furini M (2007) On ameliorating the perceived playout quality in chunk-driven P2P media streaming systems. In: Proceedings of the IEEE international conference on communications (ICC)Google Scholar
  5. 5.
    Furini M, Geraci F, Montangero M, Pellegrini M (2007) VISTO: VIsual STOryboard for web video browsing. In: Proceedings of the ACM international conference on image and video retrieval (CIVR 2007), Amsterdam, 9–11 July 2007, pp 635–642Google Scholar
  6. 6.
    Furini M, Geraci F, Montangero M, Pellegrini M (2008) On using clustering algorithms to produce video abstracts for the web scenario. In: Proceedings of the IEEE consumer communication & networking 2008 (CCNC2008), Las Vegas, IEEE Communication Society, 10–12 January 2008Google Scholar
  7. 7.
    Gao Y, Dai QH (2008) Clip based video summarization and ranking. In: Proceedings of the 2008 ACM international conference on content-based image and video retrieval (CIVR 2008), Niagara Falls, pp 135–140Google Scholar
  8. 8.
    Geraci F, Pellegrini M, Sebastiani F, Maggini M (2007) Cluster generation and cluster labeling for web snippets: a fast and accurate hierarchical solution. Internet Math 3(4):413–444MathSciNetGoogle Scholar
  9. 9.
    Girgensohn A (2003) A fast layout algorithm for visual video summaries. In: Proceedings of IEEE international conference on multimedia & expo (ICME), vol 2, pp 77–80Google Scholar
  10. 10.
    Gong Y, Liu X (2003) Video summarization and retrieval using singular value decomposition. Multimedia Syst 9(2):157–168CrossRefGoogle Scholar
  11. 11.
    Gonzalez TF (1985) Clustering to minimize the maximum intercluster distance. Theor Comp Sci 38(2/3):293–306zbMATHCrossRefGoogle Scholar
  12. 12.
    Hadi Y, Essannouni F, Thami ROH (2006) Video summarization by k-medoid clustering. In: Proceedings of the ACM symposium on applied computing, pp 1400–1401Google Scholar
  13. 13.
    Hanjalic A, Zhang HJ (1999) An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis. IEEE Trans Circuits Syst Video Technol 9(8):1280–1289CrossRefGoogle Scholar
  14. 14.
    Hochbaum DS, Shmoys DB (1985) A best possible approximation algorithm for the k-center problem. Math Oper Res 10(2):180–184zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Indyk P (1999) Sublinear time algorithms for metric space problems. In: Proceedings of ACM symposium on theory of computing, pp 428–434Google Scholar
  16. 16.
    Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11:703–715CrossRefGoogle Scholar
  17. 17.
    Müfit Ferman A, Murat Tekalp A (2003) Two-stage hierarchical video summary extraction to match low-level user browsing preferences. IEEE Trans Multimedia 5(2):244–256CrossRefGoogle Scholar
  18. 18.
    Mundur P, Rao Y, Yesha Y (2006) Keyframe-based video summarization using Delaunay clustering. Int J Digit Libr 6(2):219–232CrossRefGoogle Scholar
  19. 19.
    Nam J, Tewfik A (1999) Video abstract of video. In: IEEE 3rd workshop on multimedia signal processing, pp 117–122Google Scholar
  20. 20.
    Phillips SJ (2002) Acceleration of k-means and related clustering algorithms. In: Proceedings of 4th international workshop on algorithm engineering and experiments, San Francisco, pp 166–177Google Scholar
  21. 21.
    Ren J, Jiang J, Eckes C (2008) Hierarchical modeling and adaptive clustering for real-time summarization of rush videos in trecvid’08. In: Proc. of the 2nd ACM TRECVid video summarization workshop, Vancouver, pp 26–30Google Scholar
  22. 22.
    Shahraray B, Gibbon DC (1995) Automatic generation of pictorial transcripts of video programs. In: Proc. of multimedia computing and networking, vol 2417, pp 512–518Google Scholar
  23. 23.
    Tan Y-P, Lu H (2003) Video scene clustering by graph partitioning. Electron Lett 39(11):841–842CrossRefGoogle Scholar
  24. 24.
    The Open Video Project (2009) The Open Video Project homepage.
  25. 25.
    Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimedia Comput Commun Appl 3(1):1–37CrossRefGoogle Scholar
  26. 26.
    Van den Eijkel GC, Porskamp PAP, Van Setten M, Velthausz DD (2000) Moving storyboards: a novel approach to content-based video retrieval. Telematica Institute Internal PublicationGoogle Scholar
  27. 27.
    Xie XN, Wu F (2008) Automatic video summarization by affinity propagation clustering and semantic content mining. In: Proc. of the 2008 international symposium on electronic commerce and security, pp 203–208Google Scholar
  28. 28.
    Zhu L, Zavesky E, Shahraray E, Gibbon D, Basso A (2008) Brief and high-interest video summary generation: evaluating the AT&T labs rushes summarizations. In: Proc. of the 2nd ACM TRECVid video summarization workshop, Vancouver, pp 21–25Google Scholar
  29. 29.
    Zhuang Y, Rui Y, Huan TS, Mehrotra S (1998) Adaptive key frame extracting using unsupervised clustering. In: Proc. of the international conference on image processing, Chicago, pp 866–870Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Marco Furini
    • 1
  • Filippo Geraci
    • 2
  • Manuela Montangero
    • 3
  • Marco Pellegrini
    • 2
  1. 1.Università di Modena e Reggio EmiliaReggio EmiliaItaly
  2. 2.IIT-CNR InstitutePisaItaly
  3. 3.Università di Modena e Reggio EmiliaModenaItaly

Personalised recommendations