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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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Feature fusion and clustering for key frame extraction.

Yunyun Sun1, Peng Li2,3, Zhaohui Jiang4

  • 1School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.

Mathematical Biosciences and Engineering : MBE
|November 24, 2021
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Summary

This study introduces an Optimal Threshold and Maximum Weight (OTMW) clustering approach for automatic video summarization. The method achieves high accuracy and outperforms existing cluster-based algorithms in key-frame extraction quality.

Keywords:
Clusterfeature dataoptimizationthresholdvideo summarization

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Area of Science:

  • Computer Science
  • Multimedia Processing
  • Artificial Intelligence

Background:

  • Existing shot-based and content-based key-frame extraction methods have limitations.
  • Cluster-based algorithms offer a promising alternative for video summarization.
  • Accurate and automatic key-frame extraction is crucial for effective video summarization.

Purpose of the Study:

  • To propose an Optimal Threshold and Maximum Weight (OTMW) clustering approach for accurate and automatic video summarization.
  • To address the limitations of traditional key-frame extraction techniques.
  • To enhance the quality and relevance of extracted key-frames.

Main Methods:

  • Video content analysis using image color, texture, and information complexity to build a feature dataset.
  • Golden Section method for optimal threshold determination.
  • Improved clustering algorithm to automatically obtain initial cluster centers and number (k).
  • K-MEANS algorithm for k-cluster frame generation.
  • Maximum Weight method for representative frame extraction from each cluster.

Main Results:

  • The OTMW approach achieved a key-frame quality evaluation index of 96.11925 and an average Fidelity and Ratio of 97.128 on 16 multi-type videos.
  • Extracted key-frames demonstrated consistency with artificial visual judgment.
  • Significant improvements in Fidelity (up to 12.49721) and Ratio (average 1.958 increase) compared to state-of-the-art cluster-based algorithms.

Conclusions:

  • The proposed OTMW clustering approach provides an accurate and automatic method for video summarization.
  • The approach demonstrates superior performance over existing baselines on diverse video datasets.
  • OTMW effectively extracts representative key-frames, leading to high-quality video summaries.