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Related Experiment Video

Updated: Oct 27, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Interp-SUM: Unsupervised Video Summarization with Piecewise Linear Interpolation.

Ui-Nyoung Yoon1, Myung-Duk Hong1, Geun-Sik Jo1

  • 1Artificial Intelligence Laboratory, Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea.

Sensors (Basel, Switzerland)
|July 20, 2021
PubMed
Summary

This study introduces Interp-SUM, an unsupervised video summarization method using piecewise linear interpolation. It generates natural keyframe sequences and achieves comparable performance to state-of-the-art techniques.

Keywords:
piecewise linear interpolationreinforcement learningunsupervised learningvideo summarization

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Large-scale video browsing is challenging.
  • Effective video summarization requires selecting representative frames.
  • Unsupervised methods are needed for scalable video analysis.

Purpose of the Study:

  • To develop an unsupervised video summarization method.
  • To generate natural sequences of summary frames.
  • To improve summarization performance without manual labels.

Main Methods:

  • Proposed Interp-SUM method utilizing piecewise linear interpolation.
  • Employed a reinforcement learning framework with an explicit reward function.
  • Utilized the exploring under-appreciated reward objective and a modified reconstruction loss.

Main Results:

  • Interp-SUM generated the most natural summary frames compared to state-of-the-art methods.
  • Achieved comparable performance on SumMe and TVSum datasets.
  • Demonstrated effectiveness in unsupervised video summarization.

Conclusions:

  • Interp-SUM offers a novel approach to unsupervised video summarization.
  • The method produces natural and representative video summaries.
  • It provides a competitive alternative to existing state-of-the-art techniques.