Large-scale news topic tracking and key-scene ranking with video near-duplicate constraints
Large-scale news topic tracking and key-scene ranking with video near-duplicate constraints
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DOI：
10.1145/1631058.1631080
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Xiaomeng Wu;I. Ide;S. Satoh
中科院分区：
文献类型：
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作者：
Xiaomeng Wu;I. Ide;S. Satoh
为了充分利用当今海量的新闻视频，有必要跟踪不同渠道新闻故事的发展，挖掘它们的依赖关系，并以语义方式组织它们。我们提出了一种新颖的新闻主题跟踪和重新排名系统。主要贡献包括：（1）一种通过基于文本的近似重复进行跟踪和重新排序来挖掘主题相关故事的新颖方案，（2）一种提出的简单但有效的查询扩展算法，用于提高搜索查询的代表性，（3）一个包含超过34,000个新闻故事的大型广播视频数据库，为实验而构建，以及（4）一种用于分析文本相似性和视频近似重复约束的新颖的关键场景排序方案。
To make full use of the overwhelming volume of news videos available today, it is necessary to track the development of news stories from different channels, mine their dependencies, and organize them in a semantic way. We propose a novel news topic tracking and re-ranking system. The main contributions include: (1) a novel scheme of mining topic-related stories through tracking and re-ranking on the basis of near duplicates built on top of text, (2) a proposed simple but effective query-expansion algorithm for improving the representativeness of a search query, (3) a large-scale broadcast video database containing more than 34,000 news stories constructed for experimentation, and (4) a novel key-scene ranking scheme for analyzing both text similarity and video near-duplicate constraints.