Effective Real-Scenario Video Copy Detection

被引：0

作者：

Zhang, Yue ^{[1
]}

Zhang, Xinxiang ^{[2
]}

机构：

[1] Paytronix Syst Inc, Data Sci, Newton, MA USA

[2] Southern Methodist Univ, Lyle Sch Engn, Dallas, TX 75205 USA

来源：

2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2016年

关键词：

video copy detection; pre-defined simulated; real-scenario; VCDB;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

our task of video copy detection system aims to locate video segments that are partially copied or near-duplicated versions from an archive of reference videos. In 2010, video copy detection problem was sometimes considered as a solved problem, since previous research within this area used either small-scale or large-scale datasets(e.g. TRECVID 2009, Muscle-VCD) with pre-defined simulated videos. Therefore, the near-perfect results obtained on these datasets were somehow not convincing. As a result, in this paper, we introduce an effective real-scenario video copy detection system which aims to effectively and efficiently detect complex real video copies. Our system obtains decent results on a real-scenario large-scale video copy database (VCDB) generated in 2014, and measures the trade-off between effectiveness and efficiency. We believe our work can be regarded as the beginning for this challenging problem.

引用

页码：3951 / 3956

页数：6

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