基于3D卷积神经网络的视频哈希算法  

Video hash algorithm based on 3D convolutional neural network

作　　者：刘玉莹 刘宏哲 袁家政 李兵[3] Liu Yuying;Liu Hongzhe;Yuan Jiazheng;Li Bing(Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China;Beijing Open University,Beijing 100081,China;State Key Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]北京联合大学北京市信息服务工程重点实验室,北京100101 [2]北京开放大学,北京100081 [3]中国科学院自动化研究所模式识别国家重点实验室,北京100190

出　　处：《计算机应用研究》2020年第3期887-890,900,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61871039,61571045,6180219,6190601);国家科技支撑计划资助项目(2015BAH55F03,015BAH55F03);北京市属高校高水平教师队伍建设创新团队建设提升计划资助项目(IDHT20170511);北京市自然科学基金资助项目(4184088);北京联合大学领军人才资助项目(BPHR2019AZ01);北京市教委资助项目(KM201911417001,KM201711417005);智能贺驶大数据协同创新中心资助项目(CYXC1902);北京联合大学资助项目(WZ10201903)。

摘　　要：针对目前相似性视频检索方法中快速性和准确性仍无法满足用户要求等问题,提出了一种基于3D卷积神经网络的视频快速检索方法。该算法将3D卷积神经网络与哈希学习方法结合应用于视频数据,既能快速学习视频时空特征表示,又能极大地缩短视频检索时间。在常用视频数据集上的实验结果表明,利用所提出的方法对视频进行相似性检索性能优于当前主流方法。In order to solve the problem that the speed and accuracy of the current similarity video retrieval methods still cannot meet the requirements of users,this paper proposed a new similarity video quick retrieval method,which combined the three-dimensional convolutional neural network with the hash learning method and applied to video data.It could not only quickly learn the video spatiotemporal feature representation,but also greatly shorten the video retrieval time.The experimental results on the set show that the similarity retrieval performance of the video using the proposed method is superior to the current mainstream methods.

关 键 词：深度学习 哈希算法 视频检索 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]