WANG Xingqi, ZHA Taotao, WU Chunming, FANG Jinglong, JIANG Ming. Text Semantics Based Automatic Summarization for Chinese Videos[J]. Chinese Journal of Electronics, 2015, 24(3): 462-467. doi: 10.1049/cje.2015.07.004
Citation: WANG Xingqi, ZHA Taotao, WU Chunming, FANG Jinglong, JIANG Ming. Text Semantics Based Automatic Summarization for Chinese Videos[J]. Chinese Journal of Electronics, 2015, 24(3): 462-467. doi: 10.1049/cje.2015.07.004

Text Semantics Based Automatic Summarization for Chinese Videos

doi: 10.1049/cje.2015.07.004
Funds:  This work is supported by the National High Technology Development 863 Program of China (No.2011AA01A107), the Zhejiang Provincial Technical Plan Project (No.2011C13008, No.2013C01113) and Defense Industrial Technology Development Program (No.A3920110002, No.202012A001).
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  • Corresponding author: FANG Jinglong (corresponding author) received the Ph.D. degree from Zhejang University of Technology in 2011. Currently, he is a professor in School of Computer Science at Hangzhou Dianzi University. His research area focuses on machine learning, data mining, pattern recognition and artificial intelligence. (Email: fjl@hdu.edu.cn)
  • Received Date: 2013-11-14
  • Rev Recd Date: 2014-06-10
  • Publish Date: 2015-07-10
  • This paper is concerned with the task of automatically summarizing videos, which is important for many video-related applications. Examples include video retrieval, accessing and categorization, et al. Our approach summarizes video semantically from the texts embedded in videos. To improve the accuracy, ant colony algorithm, maximum matching method, Singular value decomposition (SVD) are employed for locating text region, segmenting words and clustering sentences, respectively. Based on these methods, a prototype is developed. In an experimental evaluation on the real world video datasets, we show that our proposed approach could provide accurate, satisfactory performance.
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