4.6 Article

Interp-SUM: Unsupervised Video Summarization with Piecewise Linear Interpolation

Journal

SENSORS
Volume 21, Issue 13, Pages -

Publisher

MDPI
DOI: 10.3390/s21134562

Keywords

video summarization; reinforcement learning; unsupervised learning; piecewise linear interpolation

Funding

  1. MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2017-0-01642]
  2. INHA UNIVERSITY Research Grant

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This paper introduces an unsupervised video summarization method, Interp-SUM, utilizing piecewise linear interpolation for generating a natural sequence of keyframes. Through reinforcement learning-based training framework, it achieves competitive performance compared to state-of-the-art methods in experiments.
This paper addresses the problem of unsupervised video summarization. Video summarization helps people browse large-scale videos easily with a summary from the selected frames of the video. In this paper, we propose an unsupervised video summarization method with piecewise linear interpolation (Interp-SUM). Our method aims to improve summarization performance and generate a natural sequence of keyframes with predicting importance scores of each frame utilizing the interpolation method. To train the video summarization network, we exploit a reinforcement learning-based framework with an explicit reward function. We employ the objective function of the exploring under-appreciated reward method for training efficiently. In addition, we present a modified reconstruction loss to promote the representativeness of the summary. We evaluate the proposed method on two datasets, SumMe and TVSum. The experimental result showed that Interp-SUM generates the most natural sequence of summary frames than any other the state-of-the-art methods. In addition, Interp-SUM still showed comparable performance with the state-of-art research on unsupervised video summarization methods, which is shown and analyzed in the experiments of this paper.

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