Shot Segmentation Based on Feature Fusion and Bayesian Online Changepoint Detection

  • Qiannan Bai
  • Fang DaiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)


Shot segmentation is an important technology in video analysis. Traditional shot segmentation methods usually need to set thresholds in advance. Due to the diversity of shot types, it is usually difficult to set appropriate thresholds for these segmentation. In this paper, a new shot segmentation method is proposed, which combines feature fusion with Bayesian online changepoint detection. Firstly, the HSV quantitative color features of video frames are extracted and a new feature MEP is constructed. The comprehensive similarity of MEP features of two adjacent frames is calculated. Then, Bayesian online changepoint detection algorithm is applied to detect the comprehensive similarity. The location of the changepoint detected is the position of shot segmentation in the video. The experimental results show that Bayesian online changepoint detection has the ability to distinguish different shots. The average Recall and Precision of our method are over 0.90, which is more accurate than the results of the methods which used to compare with our method.


Shot segmentation Bayesian online changepoint detection Feature fusion Color features 



This research was financially supported by the Xi’an Science and Technology Innovation Guidance Project (No. 201805037YD15CG21(7)).


  1. 1.
    Boreczky, J.S., Rowe, L.A.: Comparison of video shot boundary detection techniques. J. Electron. Imaging 5(2), 8–32 (1996)CrossRefGoogle Scholar
  2. 2.
    Don, A., Uma, K.: Adaptive edge-oriented shot boundary detection. EURASIP J. Image Video Process. 2009(1), 1–13 (2009)Google Scholar
  3. 3.
    Hannane, R., Elboushaki, A., Afdel, K.: Efficient video summarization based on motion SIFT-distribution histogram. In: 13th International Conference on Computer Graphics, Imaging and Visualization, Beni Mellal, Morocco, pp. 312–317. IEEE (2016)Google Scholar
  4. 4.
    Lankinen, J., Kämäräinen, J.K.: Video shot boundary detection using visual bag-of-words. In: International Conference on Computer Vision Theory and Application (2013)Google Scholar
  5. 5.
    Hanjalic, A.: Shot-boundary detection: unraveled and resolved. IEEE Trans. Circuits Syst. Video Technol. 12(2), 90–105 (2002)CrossRefGoogle Scholar
  6. 6.
    Chavan, S., Akojwar, S.: An efficient method for fade and dissolve detection in presence of camera motion & illumination. In: International Conference on Electrical, Electronics, and Optimization Techniques, pp. 3002–3007. IEEE (2016)Google Scholar
  7. 7.
    Aminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51(2), 339–367 (2017)CrossRefGoogle Scholar
  8. 8.
    Adams, R.P., Mackay, D.J.C.: Bayesian online changepoint detection. Statistics (2007)Google Scholar
  9. 9.
    Lau, H.F., Yamamoto, S.: Bayesian online changepoint detection to improve transparency in human-machine interaction systems. In: 49th IEEE Conference on Decision and Control. IEEE (2010)Google Scholar
  10. 10.
    Niekum, S., Osentoski, S., Atkeson, C.G., et al.: Online Bayesian changepoint detection for articulated motion models. In: International Conference on Robotics & Automation. IEEE (2015)Google Scholar
  11. 11.
    Gee, A.H., Chang, J., Ghosh, J., et al.: Bayesian online changepoint detection of physiological transitions. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2018)Google Scholar
  12. 12.
    Su, C.: Research and system implementation of video abstraction technology. Central South University, Hunan (2009)Google Scholar
  13. 13.
    Sun, Y., Jiang, Z., Shan, G., et al.: Key frame extraction based on optimal distance clustering and feature fusion expression. J. Nanjing Univ. Sci. Technol. 42(4), 416–423 (2018)Google Scholar
  14. 14.
    Zheng, J.: Research on no-reference image quality assessment based on image information entropy. Beijing Jiaotong University, Beijing (2015)Google Scholar
  15. 15.
    Jiang, X., Xue, H., Zhang, C., et al.: Study on influencing factors of air quality in Hohhot city based on principal component analysis. Saf. Environ. Eng. 23(01), 75–79 (2016)Google Scholar
  16. 16.
    Snelson, E., Ghahramani, Z.: Compact approximations to Bayesian predictive distributions. In: International Conference on Machine Learning, pp. 840–847. ACM (2005)Google Scholar
  17. 17.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of SciencesXi’an University of TechnologyXi’anChina

Personalised recommendations