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Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification

机译：通过标签本地化和按键识别识别事件驱动的网络视频

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摘要

With the explosive growth of web videos on the Internet, it becomes challenging to efficiently browse hundreds or even thousands of videos. When searching an event query, users are often bewildered by the vast quantity of web videos returned by search engines. Exploring such results will be time consuming and it will also degrade user experience. In this paper, we present an approach for event driven web video summarization by tag localization and key-shot mining. We first localize the tags that are associated with each video into its shots. Then, we estimate the relevance of the shots with respect to the event query by matching the shot-level tags with the query. After that, we identify a set of key-shots from the shots that have high relevance scores by exploring the repeated occurrence characteristic of key sub-events. Following the scheme in and , we provide two types of summaries, i.e., threaded video skimming and visual-textual storyboard. Experiments are conducted on a corpus that contains 60 queries and more than 10 000 web videos. The evaluation demonstrates the effectiveness of the proposed approach.
机译：随着Internet上网络视频的爆炸性增长，有效浏览数百甚至数千个视频变得越来越具有挑战性。搜索事件查询时，搜索引擎返回的大量网络视频常常使用户感到困惑。探索此类结果将非常耗时，并且还会降低用户体验。在本文中，我们提出了一种通过标签本地化和关键镜头挖掘来进行事件驱动的网络视频摘要的方法。我们首先将与每个视频相关联的标签本地化为镜头。然后，我们通过将镜头级别标签与查询相匹配，来估计镜头与事件查询的相关性。之后，我们通过探索关键子事件的重复出现特征，从具有较高相关性分数的镜头中识别出一组关键镜头。按照和中的方案，我们提供两种类型的摘要，即，螺纹视频剪辑和可视文本情节提要。实验是在一个语料库上进行的，该语料库包含60个查询和超过10,000个网络视频。评估证明了该方法的有效性。

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