Automated Video Segmentation for Lecture Videos: A Linguistics-Based Approach
Automated Video Segmentation for Lecture Videos: A Linguistics-Based Approach
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讲座视频的自动视频分割：基于语言学的方法
DOI：
10.4018/jthi.2005040102
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
J. Nunamaker
中科院分区：
文献类型：
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作者：
Ming Lin;M. Chau;Jinwei Cao;J. Nunamaker
视频是一种丰富的信息源，在学习系统中常用于获取和共享知识。然而，视频的非结构化和线性特征给终端用户在获取视频中捕获的知识方面带来了困难。为了提取隐藏在一段冗长的多主题讲座视频中的知识结构，从而使其易于访问，我们需要首先将视频按主题分割成较短的片段。由于人工分割的成本较高，因此对自动分割的要求很高。然而，目前的自动视频分割方法主要依赖场景和镜头变化检测，不适合场景/镜头变化较少、主题边界不明确的讲座视频。在本文中，我们研究了一种新的高性能的视频分割方法，该方法针对这一特殊类型的视频：讲座视频。该方法使用自然语言处理技术，如名词短语提取，并利用词汇知识来源，如WordNet。使用多个基于语言的分割特征，包括诸如名词短语的基于内容的特征和诸如线索短语的基于语篇的特征。我们的评价结果表明，名词短语的特征是显著的。
Video, a rich information source, is commonly used for capturing and sharing knowledge in learning systems. However, the unstructured and linear features of video introduce difficulties for end users in accessing the knowledge captured in videos. To extract the knowledge structures hidden in a lengthy, multi-topic lecture video and thus make it easily accessible, we need to first segment the video into shorter clips by topic. Because of the high cost of manual segmentation, automated segmentation is highly desired. However, current automated video segmentation methods mainly rely on scene and shot change detection, which are not suitable for lecture videos with few scene/shot changes and unclear topic boundaries. In this article we investigate a new video segmentation approach with high performance on this special type of video: lecture videos. This approach uses natural language processing techniques such as noun phrases extraction, and utilizes lexical knowledge sources such as WordNet. Multiple linguistic-based segmentation features are used, including content-based features such as noun phrases and discourse-based features such as cue phrases. Our evaluation results indicate that the noun phrases feature is salient.
(3) 阐明胎生鱼为未出生的孩子提供的营养
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