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Key frame extraction method for lecture videos based on spatio-temporal subtitles.

Yunzuo Zhang1, Yi Li1, Zhaoquan Cai2

  • 1Shijiazhuang Tiedao University, Shijiazhuang, People's Republic of China.

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Summary

This study introduces a novel method for extracting key frames from online lecture videos using spatio-temporal subtitles. This approach efficiently identifies crucial video segments, improving information retrieval from large lecture datasets.

Keywords:
Key frame extractionLecture videoSpatio-temporal subtitleSteady-state key frame

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Multimedia Processing

Background:

  • The COVID-19 pandemic led to a surge in online lecture videos.
  • Efficiently extracting key information from these vast video resources is challenging.

Purpose of the Study:

  • To propose a robust and efficient method for key frame extraction in lecture videos.
  • To leverage spatio-temporal subtitles for accurate video content analysis.

Main Methods:

  • Extraction and splicing of spatio-temporal subtitle slices to form a video spatio-temporal subtitle.
  • Binarization and projection methods to construct a Subtitle Spatio-temporal Projection Analysis (SSPA) curve.
  • Key frame selection using curve edge detection and subtitle existence threshold for steady-state identification.

Main Results:

  • The method achieved an average F1-score of 0.97, precision of 0.97, and recall of 0.98 across 8 test videos.
  • Demonstrated effective key frame extraction in lecture videos.
  • Significantly reduced average running time to 0.072 times that of other algorithms.

Conclusions:

  • The proposed spatio-temporal subtitle-based key frame extraction method is efficient and accurate.
  • This technique offers a robust solution for quick and precise video information retrieval.
  • The method significantly outperforms existing algorithms in terms of speed and effectiveness.