Skip to main content
SpringerLink
Log in

A novel tool for summarization of arthroscopic videos

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

Abstract

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.

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

Access this article

Log in via an institution

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
Fig. 13

Similar content being viewed by others

Notes

  1. For an in-depth explanation of surgical techniques and instruments employed in arthroscopy refer to [11]

  2. URI: http://www.youtube.com

References

  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):82–91

    Article  Google Scholar 

  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 312–322

  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 191–196

  4. Ciocca G, Schettini S (2006) An innovative algorithm for key frame extraction in video summarization. J Real-Time Image Proc 1(1):69–88

    Article  Google Scholar 

  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 1400–1401

  6. Hanjalic A, Xu LQ (2005) Affective video content representation and modeling. IEEE Trans Multimedia 7(1):143–154

    Article  Google Scholar 

  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 762–768

  8. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  9. Johnson D (2002) Basic science in digital imaging. Arthroscopy: The Journal of Arthroscopic and Related Surgery 18(6):648–653

    Article  Google Scholar 

  10. Kosch H (2004) Distributed multimedia database technologies supported by MPEG-7 and MPEG-21, CRC, Boca Raton, Florida, USA

    Google Scholar 

  11. Lajtai G, Applegate G, Snyder SJ, Aitzetmuller G, Gerber CS (eds) (2003) Shoulder arthroscopy and MRI techniques. Springer, Berlin

    Google Scholar 

  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 1085–1088

  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, 2009

  14. Manjunath BS, Salembier P, Sikora T (2002) Introduction to MPEG-7: multimedia content description interface. Wiley, Chichester, West Sussex, UK

    Google Scholar 

  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 41–45

  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):121–143

    Article  Google Scholar 

  17. NIST National Institute of Standards and Technology. Trec video retrieval evaluation. Online (last accessed on: 01/10/09): http://www-nlpir.nist.gov/projects/trecvid/

  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):639–647

    Article  Google Scholar 

  19. Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–472

    Article  Google Scholar 

  20. Text REtrieval Conference (TREC). website. http://trec.nist.gov

  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. 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 0-7803-7965-9

Download references

Author information

Authors and Affiliations

  1. Institute for Information Technology, Klagenfurt University, Universitätsstrasse 65–67, 9020, Klagenfurt, Austria

    Mathias Lux, Klaus Schöffmann & Laszlo Böszörmenyi

  2. Department of Computer Science and Engineering, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA

    Oge Marques

  3. Private Clinic Althofen, Moorweg 30, 9330, Althofen, Austria

    Georg Lajtai

Authors
  1. Mathias Lux
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oge Marques
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Klaus Schöffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laszlo Böszörmenyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Georg Lajtai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mathias Lux.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lux, M., Marques, O., Schöffmann, K. et al. A novel tool for summarization of arthroscopic videos. Multimed Tools Appl 46, 521–544 (2010). https://doi.org/10.1007/s11042-009-0353-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-009-0353-1

Keywords

Search

Navigation