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An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial

Sumandeep Kaur1, Lakhwinder Kaur2, Madan Lal2

  • 1Department of Computer Science and Engineering, Punjabi University, Patiala, 147001, India. sumandhanjal@yahoo.com.

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|November 4, 2024
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Summary

This study introduces an advanced key frame extraction method using hybrid clustering to reduce video redundancy. The technique effectively identifies and selects informative frames, improving video summarization and analysis for various applications.

Keywords:
Artificial hummingbirdFusion featuresFuzzy C-meansKey frame extractionVideo summarization

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Key frame extraction is crucial for video summarization and content-based analysis, addressing data redundancy.
  • Existing methods often rely on single visual features, limiting their effectiveness.
  • Visually impaired users can benefit from key frame extraction for object recognition and tracking.

Purpose of the Study:

  • To propose a novel key frame extraction method utilizing multiple features and advanced clustering.
  • To enhance video summarization and content analysis by reducing redundant frames.
  • To improve the accuracy and efficiency of key frame selection compared to existing techniques.

Main Methods:

  • A pre-processing step removes insignificant frames.
  • Four visual and structural features are extracted and aggregated to identify informative frames.
  • A hybrid Fuzzy C-means (FCM) and artificial hummingbird optimization algorithm (AHA) is used for frame clustering, overcoming FCM's local minima issue.
  • Key frames are selected based on Euclidean distance within clusters.

Main Results:

  • The proposed method significantly outperforms state-of-the-art techniques on Open video and YouTube datasets.
  • Evaluations show superior performance in subjective qualitative analysis and objective quantitative metrics (Precision, Recall, F-score).
  • Demonstrated applicability in real-life scenarios through testing on real videos.

Conclusions:

  • The hybrid FCM-AHA method offers an effective solution for key frame extraction.
  • The approach successfully reduces video redundancy and enhances information content.
  • This method shows promise for practical applications in video analysis and summarization.