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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)

Abstract

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.

Keywords

Shot segmentation Bayesian online changepoint detection Feature fusion Color features 

Notes

Acknowledgement

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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