Advertisement

Multimedia Tools and Applications

, Volume 76, Issue 18, pp 18409–18423 | Cite as

Storyboard-based accurate automatic summary video editing system

  • Shih-Nung Chen
Article

Abstract

The recent popularity of smart mobile devices has led to a significant increase in the needs of multimedia services. Finding new more efficient methods for automatic classification and retrieval of a large number of multimedia files will significantly reduce manpower costs. However, most current video content analysis methods adopt low-level features to analyze video frame by frame, and need to improve high-level semantic analysis on a number of issues. Hence, this study presents a storyboard-based accurate automatic summary video editing system that uses storyboard information, such as character dialogue, narration, caption, background music and shot changes, to enable accurate video content retrieval and automatic render summary videos. The proposed system can be applied to the course video trailer and the commercial video trailer for quick preview video content or suitable viewing configuration for smart mobile devices. Consequently, the audience can quickly understand the whole video story and the video editors can substantially reduce the time taken to publish videos.

Keywords

Video search Storyboard Video content retrieval Automatic video editing Summary video 

Notes

Acknowledgments

The author would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 103-2221-E-468-029.

References

  1. 1.
    Ansari A, Mohammed MH (2015) Content based video retrieval systems - methods, techniques. Trends Chall Int J Comput Appl 112(7):13–22Google Scholar
  2. 2.
    Bastan M, Cam H, Gudukbay U, Ulusoy O (2010) BilVideo-7: an MPEG-7-compatible video indexing and retrieval system. IEEE Multimed 17(3):62–73CrossRefGoogle Scholar
  3. 3.
    Belongie S, Carson C, Greenspan H, Malik J (1998) Color- and texture-based image segmentation using EM and its application to content-based image retrieval. In: Sixth International Conference on Computer Vision, pp. 675–682Google Scholar
  4. 4.
    Bhat SA, Sardessai OV, Kunde PP, Shirodkar SS (2014) Overview of existing content based video retrieval systems. Int J Adv Eng Global Technol 2(2):476–483Google Scholar
  5. 5.
    Bregler C (1997) Learning and recognizing human dynamics in video sequences. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 568–574Google Scholar
  6. 6.
    Bruno E, Marchand-Maillet S (2003) Nonlinear temporal modeling for motion-based video overviewing. In: Third International Workshop on Content-Based Multimedia IndexingGoogle Scholar
  7. 7.
    Chen LH, Chin KH, Liao HY (2008) An integrated approach to video retrieval. In: Nineteenth Conference on Australasian Database 75: 49–55Google Scholar
  8. 8.
    Deng Y, Manjunath BS (1997) content-based search of video using color, texture, and motion. In: IEEE International Conference on Image Processing 2: 534–537Google Scholar
  9. 9.
    Djerba C (2002) Content-based multimedia indexing and retrieval. IEEE Multimed 9(2):18–22CrossRefGoogle Scholar
  10. 10.
    Elgmagarmid AK, Jiang H, Helal AA, Joshi A, Admed M (1997) Video database systems: issues, products, and applications. Kluwer Academic PublishersGoogle Scholar
  11. 11.
    Hussain M, Chen D, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 80:91–106CrossRefGoogle Scholar
  12. 12.
    Ianeva T, Vries AP de, Westerveld T (2004) A dynamic probabilistic retrieval model. In: IEEE International Conference on Multimedia and Expo, pp. 1607–1610Google Scholar
  13. 13.
    Jawahar CV, Chennupati B, Paluri B, Jammalamadaka N (2005) Video retrieval based on textual queries. In: Thirteenth International Conference on Advanced Computing and CommunicationsGoogle Scholar
  14. 14.
    Lu GJ (1999) Multimedia Database Management Systems. Artech HouseGoogle Scholar
  15. 15.
    Money AG, Agius H (2008) Video summarisation: a conceptual framework and survey of the state of the art. J Vis Commun Image Represent 19:121–143CrossRefGoogle Scholar
  16. 16.
    Po LM, Ma WC (1996) A novel four-step search algorithm for fast block motion estimation. IEEE Trans Circuits Syst Video Technol 6(3):313–317CrossRefGoogle Scholar
  17. 17.
    Sebe N, Lew MS, Smeulders AWM (2003) Video retrieval and summarization. Comput Vis Image Underst 92(2–3):141–146CrossRefGoogle Scholar
  18. 18.
    Shen HT, Zhou X, Huang Z, Shao J, Zhou X (2007) UQLIPS: a real-time near-duplicate video clip detection system. In: Thirty-Third International Conference on Very Large Data Bases pp. 1374–1377Google Scholar
  19. 19.
  20. 20.
    Su JH, Hsu YT, Yeh HH, Tseng VS (2010) Retrieval using pattern indexing and matching techniques. Expert Syst Appl 37(7):5068–5085CrossRefGoogle Scholar
  21. 21.
    Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimed Comput Commun Appl 3(1):1–37CrossRefGoogle Scholar
  22. 22.
    Tusch R, Kosch H, Böszörményi L (2000) VIDEX: An integrated generic video indexing approach. In: Eighth ACM International Conference on Multimedia, pp. 448–451Google Scholar
  23. 23.
    Understanding Presentation Graphics (2016) http://www.talmanassociates.com/upg2/ch04/ch04home.cfm
  24. 24.
    Yang CW (2012) Investigation on the methods of educational films editing via semantic network concepts. National Hsinchu University of EducationGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Information CommunicationAsia UniversityTaichungRepublic of China

Personalised recommendations