Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context
Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context
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DOI：
10.1109/tmm.2008.2009673
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发表时间：
2009-02
影响因子：
7.3
通讯作者：
Xiao Wu;C. Ngo;Alexander Hauptmann;Hung-Khoon Tan
中科院分区：
文献类型：
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作者：
Xiao Wu;C. Ngo;Alexander Hauptmann;Hung-Khoon Tan
随着社交媒体的指数增长，有大量近乎简化的网络视频，从简单的格式到复杂的不同编辑效果的复杂混合物。除了丰富的视频内容外，社交网络还提供了与Web视频相关的丰富上下文信息集，例如缩略图图像，时间持续时间等。同时，Web 2.0的受欢迎程度需要及时响应用户查询。为了平衡速度和准确性方面，在本文中，我们将上下文信息从时间持续时间，视图数量和缩略图图像结合在一起，以及从颜色和本地点得出的内容分析，以实现实时接近删除的消除。从YouTube检索到的24个流行查询的结果表明，集成内容和上下文的建议方法可以以极高的效率重新对网络视频进行实时新颖性，其中大多数重复项可以迅速检测到并从最高排名中删除。 。提出的方法的加速可以比提出的有效分层方法快164倍，而性能却略有下降。
With the exponential growth of social media, there exist huge numbers of near-duplicate web videos, ranging from simple formatting to complex mixture of different editing effects. In addition to the abundant video content, the social Web provides rich sets of context information associated with web videos, such as thumbnail image, time duration and so on. At the same time, the popularity of Web 2.0 demands for timely response to user queries. To balance the speed and accuracy aspects, in this paper, we combine the contextual information from time duration, number of views, and thumbnail images with the content analysis derived from color and local points to achieve real-time near-duplicate elimination. The results of 24 popular queries retrieved from YouTube show that the proposed approach integrating content and context can reach real-time novelty re-ranking of web videos with extremely high efficiency, where the majority of duplicates can be rapidly detected and removed from the top rankings. The speedup of the proposed approach can reach 164 times faster than the effective hierarchical method proposed in , with just a slight loss of performance.