Skip to main content
SpringerLink
Log in

Key frame extraction for video summarization using local description and repeatability graph clustering

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Due to video data exponential growth supported by quick advances in multimedia technology, it became difficult for the user to retrieve information from a large videos collection. Key frame extraction consists in providing an abstract of the entire video, containing the most informative frames. In this paper, we present an efficient key frame extraction method. This method is based on local interest points description and repeatability graph clustering via approaching modularity value. To minimize the data to be treated, the process will be applied only on a set candidate frames selected with a windowing rule. Indeed, the first step consists in extracting interest points on a set of candidate frames. After that, we compute repeatability values between each two frames from the candidate set. These values are represented by a repeatability direct graph. The selection of key frames is performed using graph clustering by approaching modularity principle. The experiments performed showed that the proposed method succeeds in extracting key frames that preserve the relevant video content without redundancy.

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

Similar content being viewed by others

References

  1. Furini, M., Geraci, F., Pellegrini, M.: STIMO: still and moving video storyboard for the web scenario. Multimed. Tools Appl. 46, 47–69 (2010)

    Article  Google Scholar 

  2. Liu, X., Song, M.L., Zhang, L.M., Wang, S.L.: Joint shot boundary detection and key frame extraction. In: Proceedings of the 21st International Conference on Pattern Recognition, pp. 2565–2568 (2012)

  3. Husnain, A.M., Ashraf, M., Shakir, M., Abbas, Y., Shah, F.A.: Video summarization: techniques and classification. In: Computer Vision and Graphics, Lecture Notes in Computer Science, vol. 13(1), pp. 7594(2012)

  4. Gygli, M., Grabner, H., Riemen Schneider, H. et al.: Creating summaries from user videos. In: Computer Vision-ECCV, pp. 505–520. Springer, Berlin (2014)

  5. Tsai, C.M., Kang, L.W., Lin, C.W., et al.: Scene-based movie summarization via role-community networks. IEEE Trans. Circuits Syst. Video Technol. 23(11), 1927–1940 (2013)

    Article  Google Scholar 

  6. Boukadida, H.: Création automatique de résumés vidéo par programmation par contraintes. Doctoral dissertation, Université Rennes 1 (2015)

  7. Tonomura, Y., Akutsu, A., Otsugi, K., Sadakata, T.: VideoMAP and VideoSpaceIcon: tools for anatomizing video content. In: Proceedings of the INTERACT'93 and CHI'93 Conference on Human Factors in Computing Systems. ACM, pp. 131–136 (1993)

  8. Pentland, A., Picard, R., Davenport, G., Haase, K.: Video and image semantics: advanced tools for telecommunications. IEEE Multimed. 1, 73–75 (1994)

    Google Scholar 

  9. Rui, Y., Huang, T., Mehrotra, S.: Relevance feedback techniques in interactive content-based image retrieval. Trans. Circuits Video Technol. (1998)

  10. Barhoumi, W., Zagrouba, E.: On-the-fly extraction of key frames for efficient video summarization. In: AASRI Conference on Intelligent Systems and Control Procedia, pp. 78–84 (2013)

  11. Yuan, Z., Lu, T., Wu, D., Huang, Y., Yu, H.: Video summarization with semantic concept preservation. In: 10th International Conference on Mobile and Ubiquitous Multimedia, NY, USA, pp. 109–112

  12. Zhu, X., Loy, C.C., Gong, S.: Video synopsis by heterogeneous multi-source correlation. In: IEEE International Conference on Computer Vision, DC, USA, pp. 81–88 (2013)

  13. Omidyeganeh, M., Ghaemmaghami, S., Shirmohammadi, S.: Group-based spatio-temporal video analysis and abstraction using wavelet parameters. Signal Image Video Process. 7, 787 (2013)

    Article  Google Scholar 

  14. Sabbar, W., Chergui, A. Bekkhoucha, A.: Video summarization using shot segmentation and local motion estimation. In: International Conference on Innovative Computing Technology (INTECH), pp. 18–20 (2012)

  15. Tapu, R., Zaharia, T.: A complete framework for temporal video segmentation. In: IEEE International Conference on Consumer Electronics, pp. 156–160 (2011)

  16. Yuan, Z., Lu, T., Wu, D., Yu Huang, H.Y.: Video summarization with semantic concept preservation. In: International Conference on Mobile and Ubiquitous Multimedia, pp. 109–112 (2011)

  17. Gharbi, H., Bahroun, S., Zagrouba, E.: A novel key frame extraction approach for video summarization. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 3, pp. 148–155 (2016)

  18. Avila, E.F., Lopes, P.B., Luz Jr., A., Araújo, A.A.: VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32, 56–68 (2011)

    Article  Google Scholar 

  19. Mundur, P., Rao, Y., Yesha, Y.: Key-frame-based video summarization using delaunay clustering. Int. J. Digit. Libr. 6, 219–232 (2006)

    Article  Google Scholar 

  20. Furini, M., Geraci, F., Montangero, M., Pellegrini, M.: STIMO: STIll and MOving video storyboard for the Web scenario. In: Multimedia Tools and Applications, vol. 46, pp. 47–69. Kluwer, Dordrecht (2010)

  21. Asadi, E., Charkari, N.M.: Video summarization using fuzzy Cmeans clustering. In: IEEE 20th Iranian Conference on Electrical Engineering, pp. 690–694 (2012)

  22. Hannane, R., Elboushaki, A., Afdel, K., Naghabhushan, P., Jave, M.: An efficient method for video shot boundary detection and Key-Frame extraction using SIFT-point distribution histogram. Int J Multimed. Inf. Retr. 5(2), 89–104 (2016)

    Article  Google Scholar 

  23. Vila, M., Bardera, A., Xu, Q., et al.: Tsallis entropy-based information measures for shot boundary detection and keyframe selection. Signal Image Video Process. 7, 507 (2013)

    Article  Google Scholar 

  24. Gharbi, H., Massaoudi, M., Bahroun, S., Zagrouba, E.: Key frames extraction based on local features for efficient video summarization. In: Advanced Concepts for Intelligent Vision Systems, pp. 275–285 (2016)

  25. Barbieri, T.T., Rudinei, G.: KS-SIFT: a key-frame extraction method based on local features. In: ISM: IEEE International Symposium on Multimedia, pp. 13–17 (2014)

  26. Li, J.: Video shot segmentation and key frame extraction based on SIFT feature. In: International Conference on Image Analysis and Signal Processing, pp. 1–8 (2012)

  27. Gharbi, H., Bahroun, S., Zagrouba, E.: Robust interest points matching based on local description and spatial constraints. In: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), pp. 944331–944331. International Society for Optics and Photonics (2015)

  28. Chugh, I., Gupta, R., Kumar, R., Sahay, P.: Techniques for key frame extraction: shot segmentation and feature trajectory computation. In: IEEE International Conference in Cloud System and Big Data Engineering, pp. 463–466 (2016)

  29. Dinh, T.N., Li, X., Thai, M.T.: Network clustering via maximizing modularity: IEEE international conference on approximation algorithms and theoretical limits. In: Data Mining, pp. 101–110 (2015)

  30. Cafieri, S., Hansen, P., Liberti, L.: Improving heuristics for network modularity maximization using an exact algorithm. Discrete Appl. Math. 163, 65–72 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  31. Chen, M., Konstantin, K., Boleslaw, K.: Community detection via maximization of modularity and its variants. IEEE Trans. Comput. Soc. Syst. 1(1), 46–65 (2014)

    Article  Google Scholar 

  32. Gaskó, N., Lung, R.I., Suciu, M.: Extremal optimization and network community structure. In: Bioinspired Optimization Methods and Their Applications (2016)

  33. Schaeffer, S.E.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)

    Article  MATH  Google Scholar 

  34. The Open Video Project (OVP). http://www.open-video.org (2016)

  35. Massaoudi, M., Bahroun, S. Zagrouba, E.: Video summarization based on local features. In: International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision (2017)

  36. Cirne, M.V.M., Pedrini, H.: VISCOM: a robust video summarization approach using color co-occurrence matrices. Multimed Tools Appl. 77(1), 857–875 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire LimTic, Institut Supérieur d’Informatique (ISI), Université Tunis Elmanar, 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia

    Hana Gharbi, Sahbi Bahroun & Ezzeddine Zagrouba

Authors
  1. Hana Gharbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sahbi Bahroun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ezzeddine Zagrouba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hana Gharbi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gharbi, H., Bahroun, S. & Zagrouba, E. Key frame extraction for video summarization using local description and repeatability graph clustering. SIViP 13, 507–515 (2019). https://doi.org/10.1007/s11760-018-1376-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1376-8

Keywords

Search

Navigation