Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

STIMO: STIll and MOving video storyboard for the web scenario

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Examples of popular moving storyboards are movie trailers/previews and TV-show recaps.

  2. The threshold is determinated on a statistical base looking at distances between very similar frames.

  3. Movies have not been considered since storyboards reveal too much contents (e.g, the end of the movie), and hence ad-hoc techniques to produce highlights are more suited for this category.

  4. Results of DT are simply estimated considering that the mechanism requires between 9–10 times the video length to produce the summary [18].

References

  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 297–304

  2. Charikar MS (2002) Similarity estimation techniques from rounding algorithms. In: Proceedings of 34th annual ACM symposium on the theory of computing, Montreal, pp 380–388

  3. Feder T, Greene D (1988) Optimal algorithms for approximate clustering. In: Proceedings of the 28th ACM symposium on theory of computing, pp 434–444

  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)

  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 635–642

  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 2008

    Google Scholar 

  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 135–140

  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):413–444

    MathSciNet  Google Scholar 

  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 77–80

  10. Gong Y, Liu X (2003) Video summarization and retrieval using singular value decomposition. Multimedia Syst 9(2):157–168

    Article  Google Scholar 

  11. Gonzalez TF (1985) Clustering to minimize the maximum intercluster distance. Theor Comp Sci 38(2/3):293–306

    Article  MATH  Google Scholar 

  12. Hadi Y, Essannouni F, Thami ROH (2006) Video summarization by k-medoid clustering. In: Proceedings of the ACM symposium on applied computing, pp 1400–1401

  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):1280–1289

    Article  Google Scholar 

  14. Hochbaum DS, Shmoys DB (1985) A best possible approximation algorithm for the k-center problem. Math Oper Res 10(2):180–184

    Article  MATH  MathSciNet  Google Scholar 

  15. Indyk P (1999) Sublinear time algorithms for metric space problems. In: Proceedings of ACM symposium on theory of computing, pp 428–434

  16. Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11:703–715

    Article  Google Scholar 

  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):244–256

    Article  Google Scholar 

  18. Mundur P, Rao Y, Yesha Y (2006) Keyframe-based video summarization using Delaunay clustering. Int J Digit Libr 6(2):219–232

    Article  Google Scholar 

  19. Nam J, Tewfik A (1999) Video abstract of video. In: IEEE 3rd workshop on multimedia signal processing, pp 117–122

  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 166–177

  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 26–30

  22. Shahraray B, Gibbon DC (1995) Automatic generation of pictorial transcripts of video programs. In: Proc. of multimedia computing and networking, vol 2417, pp 512–518

  23. Tan Y-P, Lu H (2003) Video scene clustering by graph partitioning. Electron Lett 39(11):841–842

    Article  Google Scholar 

  24. The Open Video Project (2009) The Open Video Project homepage. http://www.open-video.org

  25. Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimedia Comput Commun Appl 3(1):1–37

    Article  Google Scholar 

  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 Publication

  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 203–208

  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 21–25

  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 866–870

Download references

Author information

Authors and Affiliations

  1. Università di Modena e Reggio Emilia, Reggio Emilia, Italy

    Marco Furini

  2. IIT-CNR Institute, Pisa, Italy

    Filippo Geraci & Marco Pellegrini

  3. Università di Modena e Reggio Emilia, Modena, Italy

    Manuela Montangero

Authors
  1. Marco Furini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Filippo Geraci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manuela Montangero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Pellegrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Furini.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Furini, M., Geraci, F., Montangero, M. et al. STIMO: STIll and MOving video storyboard for the web scenario. Multimed Tools Appl 46, 47–69 (2010). https://doi.org/10.1007/s11042-009-0307-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-009-0307-7

Keywords

Search

Navigation