Naming every individual in news video monologues
Naming every individual in news video monologues
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DOI：
10.1145/1027527.1027666
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发表时间：
2004-10
期刊：
影响因子：
--
通讯作者：
Jun Yang;Alexander Hauptmann
中科院分区：
文献类型：
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作者：
Jun Yang;Alexander Hauptmann
将广播新闻视频中出现的每个人与从视频抄本中检测到的名字一起识别导致更好地访问新闻视频内容。在本文中，我们用统计学习方法来解决这个具有挑战性的问题。从多种视频模式中提取的两类信息进行了探索，即功能，这有助于区分每个人的真实姓名，以及约束，这揭示了不同的人的名字之间的关系。人命名问题被制定成一个学习框架，预测最有可能的名称为每个人的基础上的功能，并使用约束条件的预测细化。在ABC World New Tonight和CNN Headline News视频上进行的实验表明，这种方法比非学习替代方法要好得多。
Naming every individual person appearing in broadcast news videos with names detected from the video transcript leads to better access of the news video content. In this paper, we approach this challenging problem with a statistical learning method. Two categories of information extracted from multiple video modalities have been explored, namely features, which help distinguish the true name of every person, as well as constraints, which reveal the relationships among the names of different persons. The person-naming problem is formulated into a learning framework which predicts the most likely name for each person based on the features, and refines the predictions using the constraints. Experiments conducted on ABC World New Tonight and CNN Headline News videos demonstrate that this approach outperforms a non-learning alternative by a large amount.