听,看,和抓到你:即时视频搜索与移动电话通过分层音视频索引

Wu Liu, Tao Mei, Yongdong Zhang, Jintao Li, Shipeng Li
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引用次数: 15

摘要

移动视频正迅速成为一种大众消费现象。越来越多的人在移动中使用智能手机搜索和浏览视频内容。在本文中,我们开发了一个创新的即时移动视频搜索系统,通过该系统,用户可以通过简单地将手机指向屏幕来捕捉他们正在观看的视频的几秒钟来发现视频。该系统能够在云中使用新的分层音视频索引方法对大规模视频数据进行索引,并实时提取轻量级的联合音视频签名,并在移动设备上进行渐进式搜索。与大多数现有的移动视频搜索应用程序只是将原始视频查询发送到云端不同,该提议的移动系统是利用移动设备的轻量级计算能力进行即时和渐进式视频搜索的首次尝试之一。该系统具有四个独特的特性:1)联合音视频签名,以应对手机捕获的查询视频所带来的巨大的视听差异;2)分层音视频索引,以整体利用音视频信号的互补性;3)轻量级指纹识别,以适应移动处理能力;4)渐进式查询过程,大大降低了计算成本,改善了用户体验——一旦获得确信的结果,搜索过程可以随时停止。我们从600小时的视频数据集中收集了由25个移动用户捕获的1400个查询视频。实验表明,当查询视频小于10秒时,系统的准确率达到90.79%,当查询视频小于5秒时,系统的准确率达到70.07%,优于现有的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Listen, look, and gotcha: instant video search with mobile phones by layered audio-video indexing
Mobile video is quickly becoming a mass consumer phenomenon. More and more people are using their smartphones to search and browse video content while on the move. In this paper, we have developed an innovative instant mobile video search system through which users can discover videos by simply pointing their phones at a screen to capture a very few seconds of what they are watching. The system is able to index large-scale video data using a new layered audio-video indexing approach in the cloud, as well as extract light-weight joint audio-video signatures in real time and perform progressive search on mobile devices. Unlike most existing mobile video search applications that simply send the original video query to the cloud, the proposed mobile system is one of the first attempts at instant and progressive video search leveraging the light-weight computing capacity of mobile devices. The system is characterized by four unique properties: 1) a joint audio-video signature to deal with the large aural and visual variances associated with the query video captured by the mobile phone, 2) layered audio-video indexing to holistically exploit the complementary nature of audio and video signals, 3) light-weight fingerprinting to comply with mobile processing capacity, and 4) a progressive query process to significantly reduce computational costs and improve the user experience---the search process can stop anytime once a confident result is achieved. We have collected 1,400 query videos captured by 25 mobile users from a dataset of 600 hours of video. The experiments show that our system outperforms state-of-the-art methods by achieving 90.79% precision when the query video is less than 10 seconds and 70.07% even when the query video is less than 5 seconds.
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