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Key frame extraction algorithm for video summarization based on key frame extraction using sliding window.

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Title: Key frame extraction algorithm for video summarization based on key frame extraction using sliding window.
Authors: Singh, Pratibha1 (AUTHOR) pratibhaparihar11@gmail.com, Kushwaha, Alok Kumar Singh1 (AUTHOR) alokkumarsingh.jk@gmail.com
Source: Multimedia Tools & Applications. Aug2025, Vol. 84 Issue 26, p31793-31812. 20p.
Subjects: Video summarization, Video processing, Multimedia systems, Algorithms, Image analysis, Machine learning
Abstract: The explosion of video content online makes finding specific information a challenge. Existing key frame extraction methods struggle to keep up with the variety of video formats and editing styles. This paper proposes CGSW-KF (Combined Gist Sliding Window Key frame), a novel key frame extraction algorithm that tackles this challenge. CGSW-KF leverages the strengths of SURF (Speeded up Robust Features) and GIST (Global Image Structure features) within a sliding window framework to accurately identify important frames. We use Dynamic Negative Sampling (DNS) to refine key frame selection, leading to a more focused and informative set of key frames. We evaluate CGSW-KF on a public dataset, demonstrating that it achieves competitive performance with deep learning models while offering better efficiency and interpretability. Our findings demonstrate the efficacy of CGSW-KF in improving video search, summarization, and indexing, hence enabling smooth navigation in the growing multimedia environment. We find an increase of 2.49% points over the state-of-the-art (SOTA). [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Aug2025, Vol. 84 Issue 26, p31793-31812. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Video+summarization%22">Video summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Video+processing%22">Video processing</searchLink><br /><searchLink fieldCode="DE" term="%22Multimedia+systems%22">Multimedia systems</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: The explosion of video content online makes finding specific information a challenge. Existing key frame extraction methods struggle to keep up with the variety of video formats and editing styles. This paper proposes CGSW-KF (Combined Gist Sliding Window Key frame), a novel key frame extraction algorithm that tackles this challenge. CGSW-KF leverages the strengths of SURF (Speeded up Robust Features) and GIST (Global Image Structure features) within a sliding window framework to accurately identify important frames. We use Dynamic Negative Sampling (DNS) to refine key frame selection, leading to a more focused and informative set of key frames. We evaluate CGSW-KF on a public dataset, demonstrating that it achieves competitive performance with deep learning models while offering better efficiency and interpretability. Our findings demonstrate the efficacy of CGSW-KF in improving video search, summarization, and indexing, hence enabling smooth navigation in the growing multimedia environment. We find an increase of 2.49% points over the state-of-the-art (SOTA). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Aug2025
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