计算机工程与应用 ›› 2020, Vol. 56 ›› Issue (19): 216-223.DOI: 10.3778/j.issn.1002-8331.1909-0105

• 图形图像处理 • 上一篇    下一篇

基于非局部3D残差网络的视频指纹算法

郭辰,李新伟,杨艺,徐良浩   

  1. 河南理工大学 电气工程与自动化学院,河南 焦作 454000
  • 出版日期:2020-10-01 发布日期:2020-09-29

Video Fingerprinting Algorithm Based on Non-local 3D Residual Network

GUO Chen, LI Xinwei, YANG Yi, XU Lianghao   

  1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, Henan 454000, China
  • Online:2020-10-01 Published:2020-09-29

摘要:

为了实现视频拷贝的快速准确检索,提出一种基于非局部3D残差网络的紧凑视频指纹。该算法以三胞胎网络架构为基础,采用非局部模块3D残差网络同时捕获视频的全局与局部时空信息,在特征提取部分末端加入量化编码层,实现了原始视频数据到离散指纹码的端到端映射;设计了由角度关系三元组损失和量化误差损失组成的网络目标函数。大量的实验结果表明,与对比算法相比,该算法在保持紧凑的同时鲁棒性与独特性均表现突出,查准率与查全率有明显提升。

关键词: 视频指纹, 非局部模块, 3D残差网络, 三元组损失, 量化误差损失

Abstract:

In order to realize fast and accurate retrieval of video copies, this paper proposes a compact video fingerprint based on non-local 3D residual network. Based on the triplet network architecture, the algorithm uses the non-local block 3D residual network to simultaneously capture the global and local spatio-temporal information of the video, and adds the quantization coding layer at the end of the feature extraction part to realize end-to-end mapping of raw video data to discrete fingerprint codes. A large number of experimental results show that compared with the comparison algorithm, the algorithm is outstanding in terms of robustness and uniqueness while maintaining compactness, and the precision and recall rate are significantly improved.

Key words: video fingerprint, non-local block, 3D residual network, triplet loss, quantization error loss