期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 55, 期 -, 页码 21-29出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2018.05.013
关键词
Video copyright; CNN; Sequence matching; SiamesLSTM
资金
- National Natural Science Foundation of China [61374194]
- National Key Science & Technology Pillar Program of China [2014BAG01DB03]
- Key Research & Development Program of Jiangsu Province [BE2016739]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
Following the rapid developments of network multimedia, video copyright protection online has become a hot topic in recent researches. However, video copy detection is still a challenging task in the domain of video analysis and computer vision, due to the large variations in scale and illumination of the copied contents. In this paper, we propose a novel deep learning based approach, in which we jointly use the Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) to solve the specific problem of detecting copied segments in videos. We first utilize a Residual Convolutional Neural Network(ResNet) to extract content features of frame-levels, and then employ a SiameseLSTM architecture for spatial-temporal fusion and sequence matching. Finally, the copied segments are detected by a graph based temporal network. We evaluate the performance of the proposed CNN-RNN based approach on a public large scale video copy dataset called VCDB, and the experiment results demonstrate the effectiveness and high robustness of our method which achieves the significant performance improvements compared to the state of the art.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据