Effective web video clustering using playlist information
Effective web video clustering using playlist information
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DOI：
10.1145/2245276.2245460
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Mariko Kamie;T. Hashimoto;H. Kitagawa
中科院分区：
文献类型：
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作者：
Mariko Kamie;T. Hashimoto;H. Kitagawa
视频共享服务的传播使得大量视频可用。即使在搜索视频共享服务时，也会返回太多视频以供查看。观众希望对视频进行分类以轻松掌握结果，因为视频可能包括不同的主题或不同的观点。视频聚类是解决这个问题的一种解决方案，有许多相关的研究方法。然而，现有的方法存在的问题：元数据中的文本信息往往是低质量的，视觉信息是难以分析的，和一些信息的用户观看行为包括噪音。播放列表信息是用户观看行为的一种。播放列表是有用的，因为它是基于观众的知识或直觉;除此之外，它不是嘈杂的。我们提出了基于播放列表的视频聚类方法（PVClustering），一种新的框架，可以形成新的集群独立的文本或视觉相似性。所提出的方法是计算成本低，语言无关。通过我们的方法，用户可以抓住搜索结果视频的轮廓在一个新的光。我们的实验表明，PVClustering生成的聚类结果良好，并证明它可以捕获视频之间的相关性或接近性，这是不是在文本信息编码。它们还展示了PVClustering的特性。
The spread of video sharing services has made available an enormous number of videos. Even when searching video sharing services, too many videos are returned to view. Viewers want to classify videos to easily grasp results, because videos may include varied topics or differing viewpoints. Video clustering is one solution to addressing this problem with many related approaches to research. However, existing approaches have problems: text information in metadata tends to be of low quality, visual information is difficult to analyze, and some information on user viewing behavior includes noise. This paper focuses on playlist information, which is a type of user viewing behavior. A playlist is useful because it is based on the viewers' knowledge or intuitiveness; beyond that, it is not noisy. We propose the playlist-based video clustering method (PVClustering), a novel framework that can form new clusters independent of text or visual similarities. The proposed method is computationally inexpensive and language-independent. By our method, users can grasp the outline of search result videos in a new light. Our experiments show good result clusters generated by PVClustering and prove that it can capture relativity or proximity among videos, which is not coded in text information. They also present the characteristics of PVClustering.