Event retrieval in video archives using rough set theory and partially supervised learning
Event retrieval in video archives using rough set theory and partially supervised learning
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DOI：
10.1007/s11042-011-0727-z
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发表时间：
2012-03
影响因子：
3.6
通讯作者：
Kimiaki Shirahama;Yuta Matsuoka;K. Uehara
中科院分区：
文献类型：
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作者：
Kimiaki Shirahama;Yuta Matsuoka;K. Uehara
本文开发了一种通过实例查询的方法，用于使用用户提供的实例镜头来检索事件的镜头（事件镜头）。主要解决以下三个问题。首先，事件镜头不能使用单个模型检索，因为它们包含由于不同的相机技术、设置等而显著不同的特征。这是克服使用粗糙集理论提取多个分类规则，每个规则专门检索事件镜头的一部分。其次，由于用户只能提供少量的示例镜头，因此通过提取的规则检索的事件镜头的量不可避免地受到限制。因此，我们将装袋和随机子空间方法。分类器根据示例镜头和特征维度来表征显著不同的事件镜头。然而，这可能导致许多不必要的镜头的潜在检索。粗糙集理论用于将联合收割机分类器组合成规则，从而提供更高的检索精度。最后，反例镜头，这是一个必要的粗糙集理论，没有提供的用户。因此，使用部分监督学习方法来从除了示例镜头之外的镜头收集这些。收集尽可能类似于示例镜头的反例镜头，因为它们对于表征事件镜头和剩余镜头之间的边界是有用的。该方法在TRECVID 2009视频数据上进行了测试。
This paper develops a query-by-example method for retrieving shots of an event (event shots) using example shots provided by a user. The following three problems are mainly addressed. Firstly, event shots cannot be retrieved using a single model as they contain significantly different features due to varied camera techniques, settings and so forth. This is overcome by using rough set theory to extract multiple classification rules with each rule specialized to retrieve a portion of event shots. Secondly, since a user can only provide a small number of example shots, the amount of event shots retrieved by extracted rules is inevitably limited. We thus incorporate bagging and the random subspace method. Classifiers characterize significantly different event shots depending on example shots and feature dimensions. However, this can result in the potential retrieval of many unnecessary shots. Rough set theory is used to combine classifiers into rules which provide greater retrieval accuracy. Lastly, counter example shots, which are a necessity for rough set theory, are not provided by the user. Hence, a partially supervised learning method is used to collect these from shots other than example shots. Counter example shots, which are as similar to example shots as possible, are collected because they are useful for characterizing the boundary between event shots and the remaining shots. The proposed method is tested on TRECVID 2009 video data.