Collection, Analysis and Summarization of Video Content

  • Arian KoźbiałEmail author
  • Mikołaj Leszczuk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 833)


Information overload is a term used to describe the difficulty of understanding when one has too much information. Information overload is one of the most common barriers in the access to e.g. video newscasts and reports. So, how a user can access and understand the overloaded information? We define the process of understanding as the assimilation of the main ideas carried by information. The best way to help and speed up understanding is summarizing the information. In this paper, we present the full scope of the summarization process, leading to a new approach for summarizing video sequences, with the special emphasis put on those with short original duration.


Video summarization Speech recognition Text boundary segmentation 



Research work funded by the National Science Center, Poland, conferred on the basis of the decision number DEC-2015/16/Z/ST7/00559.


  1. 1.
    Aghbari, Z., Kaneko, K., Makinouchi, A.: Content-trajectory approach for searching video databases. IEEE Trans. Multimedia 5(4), 516–531 (2003). Scholar
  2. 2.
    Baran, R., Rudzinski, F., Zeja, A.: Face recognition for movie character and actor discrimination based on similarity scores. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1333–1338 (2016).
  3. 3.
    Krishnappa, D.K., Bhat, D., Zink, M.: Dashing youtube: an analysis of using dash in YouTube video service. In: Proceedings of 38th Annual IEEE Conference on Local Computer Networks, vol. 1, pp. 407–415 (2013)Google Scholar
  4. 4.
    Fan, J., Elmagarmid, A.K., Zhu, X., Aref, W.G., Wu, L.: Classview: hierarchical video shot classification, indexing, and accessing. IEEE Trans. Multimedia 6(1), 70–86 (2004). Scholar
  5. 5.
    Gao, X., Tang, X.: Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing. IEEE Trans. Circuits Syst. Video Technol. 12(9), 765–776 (2002). Scholar
  6. 6.
    González-Gallardo, C.E., Torres-Moreno, J.M.: Sentence boundary detection for French with subword-level information vectors and convolutional neural networks. arXiv preprint arXiv:1802.04559 (2018)
  7. 7.
    Konopka, M.N.: Rapid object detection using a boosted cascade of simple features. Przegl. Politologiczny 2, 87–100 (2015).
  8. 8.
    Leszczuk, M., Grega, M., Koźbiał, A., Gliwski, J., Wasieczko, K., Smaïli, K.: Video summarization framework for newscasts and reports - work in progress. In: Dziech, A., Czyżewski, A. (eds.) Multimedia Communications, Services and Security, pp. 86–97. Springer, Cham (2017)CrossRefGoogle Scholar
  9. 9.
    Leszczuk, M., Papir, Z.: Protocols and systems for interactive distributed multimedia. In: Joint International Workshops on Interactive Distributed Multimedia Systems and Protocols for Multimedia Systems, IDMS/PROMS 2002 Coimbra, Portugal, November 26–29, 2002 Proceedings, chap. Accuracy vs. Speed Trade-Off in Detecting of Shots in Video Content for Abstracting Digital Video Libraries, pp. 176–189. Springer, Heidelberg.
  10. 10.
    Leszczuk, M.I., Duplaga, M.: Algorithm for video summarization of bronchoscopy procedures. Biomed. Eng. Online 10(1), 110 (2011).
  11. 11.
    Li, S., Lee, M.C.: An efficient spatiotemporal attention model and its application to shot matching. IEEE Trans. Circuits Syst. Video Technol. 17(10), 1383–1387 (2007). Scholar
  12. 12.
    Liu, T., Kender, J.R.: A hidden markov model approach to the structure of documentaries. In: 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries, pp. 111–115 (2000).
  13. 13.
    Lombardo, A., Morabito, G., Schembra, G.: Modeling intramedia and intermedia relationships in multimedia network analysis through multiple timescale statistics. IEEE Trans. Multimedia 6(1), 142–157 (2004). Scholar
  14. 14.
    Maybury, M.T., Merlino, A.E.: Multimedia summaries of broadcast news. In: Proceedings of Intelligent Information Systems, IIS 1997, pp. 442–449 (1997).
  15. 15.
    Pech-Pacheco, J.L., Cristobal, G., Chamorro-Martinez, J., Fernandez-Valdivia, J.: Diatom autofocusing in brightfield microscopy: a comparative study. In: Proceedings of 15th International Conference on Pattern Recognition. ICPR-2000, vol. 3, pp. 314–317 (2000).
  16. 16.
    Skarbek, W., Galiński, G., Wnukowicz, K.: Tree based multimedia indexing - a survey. In: Networked Audiovisual Media Technologies, Special VISNET Session at KKRRiT 2004, pp. 77–85 (2004)Google Scholar
  17. 17.
    Taskiran, C.M., Pizlo, Z., Amir, A., Ponceleon, D., Delp, E.J.: Automated video program summarization using speech transcripts. IEEE Trans. Multimedia 8(4), 775–791 (2006). Scholar
  18. 18.
    Zhang, H.J., Low, C.Y., Smoliar, S.W., Wu, J.H.: Video parsing, retrieval and browsing: an integrated and content-based solution. In: Proceedings of the Third ACM International Conference on Multimedia, MULTIMEDIA 1995, pp. 15–24. ACM, New York (1995).
  19. 19.
    Zhang, H.J., Wu, J., Zhong, D., Smoliar, S.W.: An integrated system for content-based video retrieval and browsing. Pattern Recogn. 30(4), 643–658 (1997).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland

Personalised recommendations