Keyframe Generation Method via Improved Clustering and Silhouette Coefficient for Video Summarization

Authors

  • Fengsui Wang School of Electrical Engineering, Anhui Polytechnic University, 241000 Wuhu, China; Key Laboratory of Detection Technology and Energy Saving Devices, 241000 Wuhu, China; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, 241000 Wuhu, China
  • Jingang Chen School of Electrical Engineering, Anhui Polytechnic University, 241000 Wuhu, China; Key Laboratory of Detection Technology and Energy Saving Devices, 241000 Wuhu, China; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, 241000 Wuhu, China
  • Furong Liu School of Electrical Engineering, Anhui Polytechnic University, 241000 Wuhu, China; Key Laboratory of Detection Technology and Energy Saving Devices, 241000 Wuhu, China; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, 241000 Wuhu, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2018

Keywords:

Video analysis, video summarization, hierarchical clustering, k-means clustering, silhouette coefficient

Abstract

In order to solve the issue that the traditional k-means algorithm falls into the local optimal solution in video summarization due to unreasonable initial parameter setting, a video summarization generation algorithm by using improved clustering and silhouette coefficient was proposed. Firstly, color features and texture features are extracted and fused from the decomposed video frames. Secondly, the hierarchical clustering algorithm is used to obtain the initial clustering results. And then, the improved k-means algorithm with silhouette coefficient is introduced to optimize the initial clustering results. Finally, the nearest frame from the cluster center is selected as the keyframe, and all the final keyframes are arranged in the order of the time sequence in the original video to constitute video summarization. The proposed algorithm is evaluated on the benchmark Open Video Database dataset with an average 71% precision, 84% recall rate, and 76% F-score, which is higher than state-of-the-art video summarization methods. Moreover, it generates video keyframes that are closer to user summaries, and it improves effectively the overall quality of the generated summary.

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Author Biographies

Fengsui Wang, School of Electrical Engineering, Anhui Polytechnic University, 241000 Wuhu, China; Key Laboratory of Detection Technology and Energy Saving Devices, 241000 Wuhu, China; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, 241000 Wuhu, China

Fengsui Wang received the Ph.D. degree from Nanjing University, Nanjing, China, in 2013, in circuits and systems. He was a visiting scholar at the University of Bridgeport, Connecticut, USA, in 2017. Since 2013, he has been a Faculty Member with Anhui Polytechnic University, Wuhu, China. His current research interests include the areas of image processing, video communication, machine learning and computer vision.

Jingang Chen, School of Electrical Engineering, Anhui Polytechnic University, 241000 Wuhu, China; Key Laboratory of Detection Technology and Energy Saving Devices, 241000 Wuhu, China; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, 241000 Wuhu, China

Jingang Chen received the B.S. degree from Anhui Polytechnic University, Wuhu, China, in July 2019. Now he is an M.S. candidate in Anhui Polytechnic University. His current research interests include machine learning and image processing.

Furong Liu, School of Electrical Engineering, Anhui Polytechnic University, 241000 Wuhu, China; Key Laboratory of Detection Technology and Energy Saving Devices, 241000 Wuhu, China; Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, 241000 Wuhu, China

Furong Liu received the B.S. degree from Anhui Polytechnic University, Wuhu, China, in July 2019. Now she is an M.S. candidate in Anhui Polytechnic University. Her current research interests include machine learning and video analysis.

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Published

2021-02-18

How to Cite

Wang, F., Chen, J., & Liu, F. (2021). Keyframe Generation Method via Improved Clustering and Silhouette Coefficient for Video Summarization. Journal of Web Engineering, 20(1), 147–170. https://doi.org/10.13052/jwe1540-9589.2018

Issue

Section

Advanced Practice in Web Engineering