Mining temporal information and web-casting text for automatic sports event detection
Mining temporal information and web-casting text for automatic sports event detection
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DOI：
10.1109/mmsp.2008.4665150
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发表时间：
2008-11
期刊：
影响因子：
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通讯作者：
Minh-Son Dao;N. Babaguchi
中科院分区：
文献类型：
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作者：
Minh-Son Dao;N. Babaguchi
提出了一种基于艾伦时态代数和外部文本信息支持的事件自动检测通用框架。所提出的方法的动机是（1）放松需要大量人为干预的领域知识的需要;（2）考虑到时间信息，虽然它对传达事件意义至关重要，但却很少受到关注。为了解决这两个问题，在所提出的方法中，通过使用基于Allen的非模糊时间模式的词典将事件呈现为时间序列来捕获时间信息。这些序列，然后使用挖掘类关联规则的技术，挖掘与网络广播文本支持的时间模式。然后，对先前步骤的结果进行定制以构建事件检测器。通过对30多个小时的不同广播公司和条件下的足球视频语料进行的实验和比较表明，该方法具有较高的效率、有效性和鲁棒性。
In this paper, the generic framework for automatically detecting event based on Allen temporal algebra and external text information support is presented. The motivation of the proposed method is (1) to relax the need of domain knowledge that requires significant human interference; and (2) to take into account the temporal information that has been paid less attention though it is critical to convey event meaning. In order to solve two these problems, in the proposed method, the temporal information is captured by presenting events as the temporal sequences using a lexicon of Allen-based non-ambiguous temporal patterns. These sequences are then used to mine temporal patterns with web-casting text supports by using technique of mining class association rules. Then, the results of previous steps are tailored to build the event detector. Thorough experimental results and comparisons that are carried on more than 30 hours of soccer video corpus captured at different broadcasters and conditions demonstrates that the proposed method meets two aforementioned motivations with high efficiency, effectiveness, and robustness.