Video retrieval by using video ontology

在视频中，同一事件可以通过不同的摄像技术和不同的情况下拍摄。因此，同一事件的镜头可能包含显著不同的特征。为了检索这样不同的镜头集为一个给定的事件（查询），我们提出了一种方法，它定义了一个基于粗糙集理论的事件。首先，给定事件的镜头子集作为正例，我们将事件表示为子集的并集。然后，我们采用部分监督学习方法从大量未标记数据中获取负样本。更准确地说，我们从未标记的数据中识别出“可能的”负例，这是基于它们与给定正例的不同之处。在计算相异性时，我们利用子空间聚类在高维特征空间的不同子空间中找到聚类。

In videos, the same event can be taken by different camera techniques and in different situations. Therefore, shots of the same event may contain significantly different features. In order to retrieve such diverse sets of shots for a given event (query), we propose a method which defines an event based on the rough set theory. First, given subsets of shots for an event as positive examples, we represent the event as the union of the subsets. Then, we adopt a partially supervised learning approach to obtain negative examples from a large amount of unlabeled data. To be precise, we identify "likely" negative examples from the unlabeled data based on their dissimilarities to the given positive examples. In calculating dissimilarities, we take advantage of subspace clustering to find clusters in different subspaces of the high-dimensional feature space.