Feature fusion and clustering for key frame extraction

Numerous limitations of Shot-based and Content-based key-frame extraction approaches have encouraged the development of Cluster-based algorithms. This paper proposes an Optimal Threshold and Maximum Weight (OTMW) clustering approach that allows accurate and automatic extraction of video summarizatio...

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Autores principales:	Yunyun Sun, Peng Li, Zhaohui Jiang, Sujun Hu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	cluster feature data threshold optimization video summarization Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/9e5d6f9f890e44cbb54cf572d1df78f7
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id	oai:doaj.org-article:9e5d6f9f890e44cbb54cf572d1df78f7
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spelling	oai:doaj.org-article:9e5d6f9f890e44cbb54cf572d1df78f72021-11-29T05:57:03ZFeature fusion and clustering for key frame extraction10.3934/mbe.20214571551-0018https://doaj.org/article/9e5d6f9f890e44cbb54cf572d1df78f72021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021457?viewType=HTMLhttps://doaj.org/toc/1551-0018Numerous limitations of Shot-based and Content-based key-frame extraction approaches have encouraged the development of Cluster-based algorithms. This paper proposes an Optimal Threshold and Maximum Weight (OTMW) clustering approach that allows accurate and automatic extraction of video summarization. Firstly, the video content is analyzed using the image color, texture and information complexity, and video feature dataset is constructed. Then a Golden Section method is proposed to determine the threshold function optimal solution. The initial cluster center and the cluster number k are automatically obtained by employing the improved clustering algorithm. k-clusters video frames are produced with the help of K-MEANS algorithm. The representative frame of each cluster is extracted using the Maximum Weight method and an accurate video summarization is obtained. The proposed approach is tested on 16 multi-type videos, and the obtained key-frame quality evaluation index, and the average of Fidelity and Ratio are 96.11925 and 97.128, respectively. Fortunately, the key-frames extracted by the proposed approach are consistent with artificial visual judgement. The performance of the proposed approach is compared with several state-of-the-art cluster-based algorithms, and the Fidelity are increased by 12.49721, 10.86455, 10.62984 and 10.4984375, respectively. In addition, the Ratio is increased by 1.958 on average with small fluctuations. The obtained experimental results demonstrate the advantage of the proposed solution over several related baselines on sixteen diverse datasets and validated that proposed approach can accurately extract video summarization from multi-type videos.Yunyun Sun Peng LiZhaohui Jiang Sujun HuAIMS Pressarticleclusterfeature datathresholdoptimizationvideo summarizationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9294-9311 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cluster feature data threshold optimization video summarization Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	cluster feature data threshold optimization video summarization Biotechnology TP248.13-248.65 Mathematics QA1-939 Yunyun Sun Peng Li Zhaohui Jiang Sujun Hu Feature fusion and clustering for key frame extraction
description	Numerous limitations of Shot-based and Content-based key-frame extraction approaches have encouraged the development of Cluster-based algorithms. This paper proposes an Optimal Threshold and Maximum Weight (OTMW) clustering approach that allows accurate and automatic extraction of video summarization. Firstly, the video content is analyzed using the image color, texture and information complexity, and video feature dataset is constructed. Then a Golden Section method is proposed to determine the threshold function optimal solution. The initial cluster center and the cluster number k are automatically obtained by employing the improved clustering algorithm. k-clusters video frames are produced with the help of K-MEANS algorithm. The representative frame of each cluster is extracted using the Maximum Weight method and an accurate video summarization is obtained. The proposed approach is tested on 16 multi-type videos, and the obtained key-frame quality evaluation index, and the average of Fidelity and Ratio are 96.11925 and 97.128, respectively. Fortunately, the key-frames extracted by the proposed approach are consistent with artificial visual judgement. The performance of the proposed approach is compared with several state-of-the-art cluster-based algorithms, and the Fidelity are increased by 12.49721, 10.86455, 10.62984 and 10.4984375, respectively. In addition, the Ratio is increased by 1.958 on average with small fluctuations. The obtained experimental results demonstrate the advantage of the proposed solution over several related baselines on sixteen diverse datasets and validated that proposed approach can accurately extract video summarization from multi-type videos.
format	article
author	Yunyun Sun Peng Li Zhaohui Jiang Sujun Hu
author_facet	Yunyun Sun Peng Li Zhaohui Jiang Sujun Hu
author_sort	Yunyun Sun
title	Feature fusion and clustering for key frame extraction
title_short	Feature fusion and clustering for key frame extraction
title_full	Feature fusion and clustering for key frame extraction
title_fullStr	Feature fusion and clustering for key frame extraction
title_full_unstemmed	Feature fusion and clustering for key frame extraction
title_sort	feature fusion and clustering for key frame extraction
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/9e5d6f9f890e44cbb54cf572d1df78f7
work_keys_str_mv	AT yunyunsun featurefusionandclusteringforkeyframeextraction AT pengli featurefusionandclusteringforkeyframeextraction AT zhaohuijiang featurefusionandclusteringforkeyframeextraction AT sujunhu featurefusionandclusteringforkeyframeextraction
_version_	1718407602019237888

Feature fusion and clustering for key frame extraction

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