A probabilistic framework for fusing frame-based searches within a video copy detection system
A probabilistic framework for fusing frame-based searches within a video copy detection system
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DOI：
10.1145/1386352.1386384
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发表时间：
2008-07
期刊：
影响因子：
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通讯作者：
N. Gengembre;Sid-Ahmed Berrani
中科院分区：
文献类型：
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作者：
N. Gengembre;Sid-Ahmed Berrani
在过去的几年中，基于内容的视频副本检测成为解决视频版权保护问题的重要和关键工具。随着网络视频交换平台的开发，此问题已加剧。通常，基于内容的视频副本检测取决于视频的视觉内容的描述。该视频已分割，描述了所选框架的子集，称为KeyFrames。然后，搜索视频将通过基于密钥帧的一组相似性搜索执行。这些搜索提供了必须集成和融合的部分结果。在本文中，我们专注于这个特殊而关键的步骤。目的是将部分结果正确融合在一起，以考虑视频的时间连贯性并高效（即快速）。我们提出的解决方案是基于一个概率框架，该框架建模了此步骤的不同参数和输入，并可以处理时间一致性。这也使过程更加可靠，因为基于关键帧的相似性搜索在基于关键的相似性搜索过程中的不精确没有影响整体准确性。这特别允许加速检测过程。
In the last few years, content-based video copy detection became an important and key tool for solving the tricky problem of video copyright protection. This problem has been heightened with the development of web video exchange platforms. In general, content-based video copy detection relies on the description of the visual content of the video. The video is segmented and a selected subset of frames, called keyframes, is described. Searching for a video is then performed by a set of similarity searches based on keyframes. These searches provide partial results that have to be integrated and fused. In this paper, we focus on this particular and crucial step. The objective is to properly fuse together partial results, to take into account the temporal coherence of the video and to be efficient (i.e. rapid). The solution we propose is based on a probabilistic framework that models the different parameters and inputs of this step and enables to deal with the temporal consistency. It also makes the process more reliable, as imprecision tolerated during the keyframe-based similarity searches has no impact on the overall accuracy. This particularly allows the speeding up of the detection process.