基于视觉特征和标签共现的大规模网络视频镜头排序

Do Hang Nga, Keiji Yanai
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引用次数: 2

摘要

在本文中,我们提出了一种新的排序方法,VisualTextualRank,它扩展了[1]和[2]。我们的方法是基于二部图上的随机行走来有效地整合视频镜头的视觉信息和网络视频的标签信息。需要注意的是,我们没有将文本信息作为镜头排序的附加特征,而是探索了镜头与对应视频的文本信息之间的相互强化,从而提高了镜头排序。我们将提出的方法应用于从Web视频[3]中自动提取特定动作的相关视频片段的系统。基于实验结果,我们证明了我们的排序方法可以提高视频镜头检索的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-scale web video shot ranking based on visual features and tag co-occurrence
In this paper, we propose a novel ranking method, VisualTextualRank, which extends [1] and [2]. Our method is based on random walk over 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 apply our proposed method to the system of extracting automatically relevant video shots of specific actions from Web videos [3]. Based on our experimental results, we demonstrate that our ranking method can improve the performance of video shot retrieval.
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