Key Frame Extraction Based on Motion Vector

  • Ziqian Qiang
  • Qing XuEmail author
  • Shihua Sun
  • Mateu Sbert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9917)


Video key frame extraction is a video summarization technique which is used to recapitulate and describe the most important segments of video sequence. Video clips containing motion information are more likely to draw users’ attention. Accordingly, we propose a novel key frame extraction scheme based on motion vector. A video sequence is partitioned into shots by distance between consecutive video frames calculated by Relative Entropy (RE). Difference of magnitude of motion vector between neighboring video images within a shot is computed to localize video clips containing significant content changes. And such segments are defined as active sub-shots in this paper. The key frames are extracted from each active sub-shots and other inactive sub-shots by exploiting different algorithms. Experimental results show that our proposed method obtains exact and complete results in video key frame extraction.


Key frame extraction Motion vector 



This work has been funded by Natural Science Foundation of China (61471261, 61179067, U1333110), and by grants TIN2013-47276-C6-1-R from Spanish Government and 2014-SGR-1232 from Catalan Government (Spain).


  1. 1.
    Chang, H.S., Sull, S., Lee, S.U.: Efficient video indexing scheme for content-based retrieval. IEEE Trans. Circ. Syst. Video Technol. 9, 1269–1279 (1999)CrossRefGoogle Scholar
  2. 2.
    Escolano, F., Suau, P., Bonev, B.: Information Theory in Computer Vision and Pattern Recognition. Springer, London (2009)CrossRefzbMATHGoogle Scholar
  3. 3.
    Feixas, M., Bardera, A., Rigau, J., Xu, Q., Sbert, M.: Information Theory Tools for Image Processing. Morgan & Claypool, San Rafael (2014)zbMATHGoogle Scholar
  4. 4.
    Gianluigi, C., Raimondo, S.: An innovative algorithm for key frame extraction in video summarization. J. Real-Time Image Proc. 1, 69–88 (2006)CrossRefGoogle Scholar
  5. 5.
    Guo, Y., Xu, Q., Sun, S., Luo, X., Sbert, M.: Selecting video key frames based on relative entropy and the extreme studentized deviate test. Entropy 18(3), 73 (2016)CrossRefGoogle Scholar
  6. 6.
    Li, R., Zeng, B., Liou, M.L.: A new three-step search algorithm for block motion estimation. IEEE Trans. Circ. Syst. Video Technol. 4(4), 438–442 (1994)CrossRefGoogle Scholar
  7. 7.
    Lienhart, R., Pfeiffer, S., Effelsberg, W.: Video abstracting. Commun. ACM 40, 54–62 (1997)CrossRefGoogle Scholar
  8. 8.
    Liu, T., Kender, J.R.: Computational approaches to temporal sampling of video sequences. ACM Trans. Multimedia Comput. Commun. Appl. 3, 217–218 (2007)CrossRefGoogle Scholar
  9. 9.
    Ma, Y.F., Lu, L., Zhang, H.J., Li, M.: A user attention model for video summarization. In: Proceedings of the Tenth ACM International Conference on Multimedia, pp. 533–542. ACM (2002)Google Scholar
  10. 10.
    Mentzelopoulos, M., Psarrou, A.: Key-frame extraction algorithm using entropy difference. In: Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 39–45. ACM. New York, October 2004Google Scholar
  11. 11.
    Money, A.G., Agius, H.: Video summarisation: a conceptual framework and survey of the state of the art. J. Vis. Commun. Image Represent. 19, 121–143 (2008)CrossRefGoogle Scholar
  12. 12.
    Ngo, C.W., Ma, Y.F., Zhang, H.J.: Video summarization and scene detection by graph modeling. IEEE Trans. Circ. Syst. Video Technol. 15(2), 296–305 (2005)CrossRefGoogle Scholar
  13. 13.
    Omidyeganeh, M., Ghaemmaghami, S., Shirmohammadi, S.: Video keyframe analysis using a segment-based statistical metric in a visually sensitive parametric space. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 20, 2730–2737 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Souza, C.L., Pádua, F.L.C., Nunes, C.F.G., Assis, G.T., Silva, G.D.: A unified approach to content-based indexing and retrieval of digital videos from television archives. Artif. Intell. Res. 3(3), 49–61 (2014)CrossRefGoogle Scholar
  15. 15.
    Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimedia Comput. Commun. Appl. 3, 3 (2007)CrossRefGoogle Scholar
  16. 16.
    C̆erneková, Z., Pitas, I., Nikou, C.: Information theory-based shot cut/fade detection and video summarization. IEEE Trans. Circ. Syst. Video Technol. 16, 82–91 (2006)CrossRefGoogle Scholar
  17. 17.
    Xu, Q., Liu, Y., Li, X., Yang, Z., Wang, J., Sbert, M., Scopigno, R.: Browsing and exploration of video sequences: a new scheme for key frame extraction and 3D visualization using entropy based Jensen divergence. Inf. Sci. 278, 736–756 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ziqian Qiang
    • 1
  • Qing Xu
    • 1
    Email author
  • Shihua Sun
    • 1
  • Mateu Sbert
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Graphics and Imaging LabUniversitat de GironaGironaSpain

Personalised recommendations