VisualTextualRank : An Extension of VisualRank to Large-Scale Video Shot Extraction Exploiting Tag Co-occurrence ∗
VisualTextualRank : An Extension of VisualRank to Large-Scale Video Shot Extraction Exploiting Tag Co-occurrence ∗
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VisualTextualRank：VisualRank 到利用标签共现的大规模视频镜头提取的扩展*
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Nga H. DO†a
中科院分区：
文献类型：
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作者：
Nga H. DO†a
在本文中，我们提出了一种称为 VisualTextualRank 的新颖排名方法，该方法根据数据与指定关键字之间的相关性对媒体数据进行排名。我们将我们的方法应用于视频镜头排名系统，该系统旨在从网络视频中自动获取与给定动作关键词相对应的视频镜头。关键词可以是任何类型的动作，例如“冲浪”（运动动作）或“刷牙”（日常活动）。排名靠前的视频镜头预计与关键词相关。虽然我们的基线仅利用数据的视觉特征，但所提出的方法同时利用文本信息（标签）和视觉特征。我们的方法基于二分图上的随机游走，有效地整合视频镜头的视觉信息和网络视频的标签信息。请注意，我们没有将文本信息视为镜头排名的附加特征，而是探索镜头与其相应视频的文本信息之间的相互强化，以提高镜头排名。我们在基线使用的数据库上验证了我们的框架。实验表明，我们提出的排名方法 VisualTextualRank 显着提高了视频镜头提取系统的性能。关键词: 镜头排序, 标签共现, 视觉特征, 二分图
In this paper, we propose a novel ranking method called VisualTextualRank which ranks media data according to the relevance between the data and specified keywords. We apply our method to the system of video shot ranking which aims to automatically obtain video shots corresponding to given action keywords from Web videos. The keywords can be any type of action such as “surfing wave” (sport action) or “brushing teeth” (daily activity). Top ranked video shots are expected to be relevant to the keywords. While our baseline exploits only visual features of the data, the proposed method employs both textual information (tags) and visual features. Our method is based on random walks over a bipartite graph to integrate visual information of video shots and tag information of Web videos effectively. Note that instead of treating the textual information as an additional feature for shot ranking, we explore the mutual reinforcement between shots and textual information of their corresponding videos to improve shot ranking. We validated our framework on a database which was used by the baseline. Experiments showed that our proposed ranking method, VisualTextualRank, improved significantly the performance of the system of video shot extraction over the baseline. key words: shot ranking, tag co-occurence, visual features, bipartite graph
影响因子：
19.5
作者：
Niebles, Juan Carlos;Wang, Hongcheng;Fei-Fei, Li
通讯作者：
Fei-Fei, Li