Automated summarization of narrative video on a semantic level
Automated summarization of narrative video on a semantic level
复制标题
语义层面的叙事视频自动摘要
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
H. Weda
中科院分区：
文献类型：
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作者：
T. Tsoneva;M. Barbieri;H. Weda
电影业每年制作数以千计的故事片和电视剧。如此海量的数据量将需要消费者一生以上的时间才能看到。因此，致力于提供简洁和信息丰富的视频摘要的叙事媒体摘要成为一个热门的研究课题。然而，到目前为止，大多数摘要解决方案都旨在以牺牲故事情节为代价，仅代表视频的整体氛围。在本文中，我们描述了一种自动生成叙事视频摘要的新方法。我们提出了一个用于创建运动图像摘要的自动化内容分析和摘要框架。我们的目标是将故事情节保留到用户可以观看摘要而不是原始内容的水平。我们的解决方案是基于字幕和电影剧本中提供的文本提示。我们提取关键字、主要人物姓名和存在等特征，并将它们组合在一个重要性函数中，以识别与保留故事情节最相关的时刻。我们开发了几种摘要方法，并通过用户测试从用户理解和用户满意度方面对结果摘要的质量进行了评估。
The movie industry produces thousands of feature films and TV series annually. Such massive data volumes would take consumers more than a lifetime to watch. Therefore, summarization of narrative media, which engages in providing concise and informative video summaries, has become a popular topic of research. However, most of the summarization solutions so far aim to represent just the overall atmosphere of the video at the expense of the story line. In this paper we describe a novel approach for automated creation of summaries for narrative videos. We propose an automated content analysis and summarization framework for creating moving-image summaries. We aim at preserving the story line to the level that users can watch the summary instead of the original content. Our solution is based on textual cues available in subtitles and movie scripts. We extract features like keywords, main characters names and presence, and combine them in an importance function to identify the moments most relevant for preserving the story line. We develop several summarization methods and evaluate the quality of the resulting summaries in terms of user understanding and user satisfaction through a user test.