%0 Journal Article %T 一种多模态融合的网络视频相关性度量方法 %A 温有福 %A 贾彩燕 %A 陈智能 %J 智能系统学报 %D 2016 %R 10.11992/tis.201603040 %X 随着网络和多媒体技术的发展,视频分享网站中的网络视频数量呈爆炸式增长。海量视频库中的高精度视频检索、分类、标注等任务成为亟待解决的研究问题。视频间的相关性度量是这些问题所面临的一个共性基础技术。本文从视频视觉内容,视频标题和标签文本,以及视频上传时间、类别、作者3种人与视频交互产生的社会特征等多源异构信息出发,提出一种新颖的多模态融合的网络视频相关性度量方法,并将所获相关性应用到大规模视频检索任务中。YouTube数据上的实验结果显示:相对于传统单一文本特征、单一视觉特征的检索方案,以及文本和视觉特征相融合的检索方案,文本视觉和用户社会特征多模态融合方法表现出更好的性能。</br>With the advances in internet and multimedia technologies, the number of web videos on social video platforms rapidly grows. Therefore, tasks such as large-scale video retrieval, classification, and annotation become issues that need to be urgently addressed. Web video relatedness serves as a basic and common infrastructure for these issues. This paper investigates the measurement of web video relatedness from a multi-modal fusion perspective. It proposes to measure web video relatedness based on multi-source heterogeneous information. The multi-modal fusion simultaneously leverages videos’ visual content, title, and tag text as well as social features contributed by human-video interactions (i.e., the upload time, channel, and author of a video). Consequently, a novel multi-modal fusion approach is proposed for computing web video relatedness, which serves to give a ranking criterion and is applied to the task of large-scale video retrieval. Experimental results using YouTube videos show that the proposed text, visual, and users’ social feature multi-modal fusion approach performs best in comparison tests with three alternate approaches; i.e., those approaches that compute web video relatedness based just on text features, just on visual features, or jointly on text and visual features %K 网络视频 %K 海量视频 %K 社会特征 %K 交互 %K 多源异构信息 %K 多模态信息融合 %K 相关性度量 %K 视频检索< %K /br> %K web video %K large-scale video %K social feature %K human-video interactions %K multi-source heterogeneous information %K social features %K multi-modal fusion %K relatedness measurement %K video retrieval %U http://tis.hrbeu.edu.cn/oa/darticle.aspx?type=view&id=20160311