Multimedia Tools and Applications

, Volume 46, Issue 2–3, pp 521–544 | Cite as

A novel tool for summarization of arthroscopic videos

  • Mathias LuxEmail author
  • Oge Marques
  • Klaus Schöffmann
  • Laszlo Böszörmenyi
  • Georg Lajtai


Arthroscopic surgery is a minimally invasive procedure that uses a small camera to generate video streams, which are recorded and subsequently archived. In this paper we present a video summarization tool and demonstrate how it can be successfully used in the domain of arthroscopic videos. The proposed tool generates a keyframe-based summary, which clusters visually similar frames based on user-selected visual features and appropriate dissimilarity metrics. We discuss how this tool can be used for arthroscopic videos, taking advantage of several domain-specific aspects, without losing its ability to work on general-purpose videos. Experimental results confirm the feasibility of the proposed approach and encourage extending it to other application domains.


Multimedia tools Video summarization Arthroscopic videos 


  1. 1.
    Cerneková Z, Pitas I, Nikou C (2006) Information theory-based shot cut/fade detection and video summarization. IEEE Trans Circuits Syst Video Technol 16(1):82–91CrossRefGoogle Scholar
  2. 2.
    Chatzichristofis SA, Boutalis YS (2008) CEDD: Color and Edge Directivity Descriptor. A compact descriptor for image indexing and retrieval. In: Gasteratos A, Vincze M, Tsotsos JK (eds) Proceedings of the 6th International Conference on Computer Vision Systems, ICVS 2008, Springer, Santorini, Greece, pp 312–322Google Scholar
  3. 3.
    Chatzichristofis SA, Boutalis YS (2008) FCTH: Fuzzy Color and Texture Histogram. A low level feature for accurate image retrieval. In: Proceedings of the 9th International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2008, IEEE, Klagenfurt, Austria, pp 191–196Google Scholar
  4. 4.
    Ciocca G, Schettini S (2006) An innovative algorithm for key frame extraction in video summarization. J Real-Time Image Proc 1(1):69–88CrossRefGoogle Scholar
  5. 5.
    Hadi Y, Essannouni F, Thami ROH (2006) Video summarization by k-medoid clustering. In: SAC ’06: Proceedings of the 2006 ACM symposium on applied computing, ACM, New York, NY, USA, pp 1400–1401Google Scholar
  6. 6.
    Hanjalic A, Xu LQ (2005) Affective video content representation and modeling. IEEE Trans Multimedia 7(1):143–154CrossRefGoogle Scholar
  7. 7.
    Huang J, Kumar SR, Mitra M, Zhu W-J, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, CVPR ’97, IEEE, San Juan, Puerto Rico, pp 762–768Google Scholar
  8. 8.
    Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRefGoogle Scholar
  9. 9.
    Johnson D (2002) Basic science in digital imaging. Arthroscopy: The Journal of Arthroscopic and Related Surgery 18(6):648–653CrossRefGoogle Scholar
  10. 10.
    Kosch H (2004) Distributed multimedia database technologies supported by MPEG-7 and MPEG-21, CRC, Boca Raton, Florida, USAGoogle Scholar
  11. 11.
    Lajtai G, Applegate G, Snyder SJ, Aitzetmuller G, Gerber CS (eds) (2003) Shoulder arthroscopy and MRI techniques. Springer, BerlinGoogle Scholar
  12. 12.
    Lux M, Chatzichristofis SA (2008) Lire: lucene image retrieval: an extensible java CBIR library. In: MM ’08: Proceeding of the 16th ACM international conference on Multimedia, ACM, New York, NY, USA, pp 1085–1088Google Scholar
  13. 13.
    Lux M, Schöffmann K, Marques O, Böszörmenyi L (2009) A novel tool for quick video summarization using keyframe extraction techniques. In: Proceedings of the 9th Workshop on Multimedia Metadata (WMM’09), CEUR Workshop Proceedings, Vol. 441, Toulouse, France, March 19–20, 2009Google Scholar
  14. 14.
    Manjunath BS, Salembier P, Sikora T (2002) Introduction to MPEG-7: multimedia content description interface. Wiley, Chichester, West Sussex, UKGoogle Scholar
  15. 15.
    Matos N, Pereira F (2008) Using MPEG-7 for generic audiovisual content automatic summarization. In: Image analysis for multimedia interactive services, 2008. WIAMIS’08. Ninth International Workshop on, pp 41–45Google Scholar
  16. 16.
    Money AG, Agius H (2008) Video summarisation: a conceptual framework and survey of the state of the art. J Vis Commun Image Represent 19(2):121–143CrossRefGoogle Scholar
  17. 17.
    NIST National Institute of Standards and Technology. Trec video retrieval evaluation. Online (last accessed on: 01/10/09):
  18. 18.
    Pavlovich R, Vazquez-Vela G, Pardinas J, Bustos Villarreal J, Rico E, de la Mora Behar G (2002) Basic science in digital imaging. Arthroscopy: The Journal of Arthroscopic and Related Surgery 18(6):639–647CrossRefGoogle Scholar
  19. 19.
    Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–472CrossRefGoogle Scholar
  20. 20.
    Text REtrieval Conference (TREC). website.
  21. 21.
    Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimed Comput Comm Appl (TOMCCAP) 3(1). doi: 10.1145/1198302.1198305
  22. 22.
    Xu M, Maddage NC, Xu C, Kankanhalli M, Tian Q (2003) Creating audio keywords for event detection in soccer video. In: ICME ’03: Proceedings of the 2003 International Conference on Multimedia and Expo, Vol. 1. IEEE Computer Society, Washington, DC, USA, pp 281–284, isbn 0-7803-7965-9Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mathias Lux
    • 1
    Email author
  • Oge Marques
    • 2
  • Klaus Schöffmann
    • 1
  • Laszlo Böszörmenyi
    • 1
  • Georg Lajtai
    • 3
  1. 1.Institute for Information TechnologyKlagenfurt UniversityKlagenfurtAustria
  2. 2.Department of Computer Science and EngineeringFlorida Atlantic UniversityBoca RatonUSA
  3. 3.Private Clinic AlthofenAlthofenAustria

Personalised recommendations