Query-based video event definition using rough set theory
Query-based video event definition using rough set theory
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DOI：
10.1145/1631024.1631029
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Kimiaki Shirahama;C. Sugihara;Yuta Matsuoka;K. Uehara
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文献类型：
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作者：
Kimiaki Shirahama;C. Sugihara;Yuta Matsuoka;K. Uehara
由于用户查询的事件范围很广，因此用预定义事件索引视频档案是不切实际的。因此，“基于查询的事件定义”是一项基本技术，其中事件是根据用户提供的示例视频来定义的。在本文中，我们介绍了一种新颖的基于查询的事件定义方法，以涵盖同一事件中低级特征的大量变化。具体来说，由于视频制作技术的任意性，同一事件的镜头包含显着不同的低级特征。因此，我们假设这些镜头分布在特征空间的不同子集中。为了提取这样的子集，我们将“粗糙集理论”应用于与事件相关的示例镜头（正例）和不相关的示例镜头（负例）。因此，我们可以提取不同的子集，其中可以通过由特定低级特征组成的“决策规则”正确分类正例或负例。在此过程中，为了避免提取过于专业的决策规则，我们使用“多重对应分析（MCA）”区分重要的低级特征和不重要的特征。最后，根据决策规则对视频档案进行搜索。 TRECVID 2008 视频档案上的实验结果表明我们的方法实现每个事件的广泛覆盖的可能性。
Since events queried by a user range much widely, indexing a video archive with pre-defined events is impractical. Hence, "query-based event definition" is an essential technique where an event is defined from example videos provided by the user. In this paper, we introduce a novel query-based event definition method to cover a large variation of low-level features in the same event. Specifically, due to arbitrary video production techniques, shots of the same event contain significantly different low-level features. Thus, we assume that these shots are distributed in different subsets in a feature space. To extract such subsets, we apply "rough set theory" to example shots relevant to an event (positive examples) and irrelevant example shots (negative examples). Thereby, we can extract different subsets where positive or negative examples can be correctly classified by "decision rules" consisting of specific low-level features. In this process, to avoid extracting over-specialized decision rules, we distinguish important low-level features from unimportant ones using "Multiple Correspondence Analysis (MCA)". Finally, the video archive is searched based on decision rules. Experimental results on TRECVID 2008 video archive show the possibility of our method to achieve the wide coverage of each event.