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Video Shot Boundary Detection Using Normalized Periodogram Distance Metric

机译:使用归一化周期图距离度量的视频镜头边界检测

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Video shot boundary detection is the primary task for content based video management and retrieval system. This paper proposes a shot boundary detection strategy by exploiting the pros of Normalized Periodogram for efficiently representing the content of the video. A Normalized Periodogram based distance metric to detect the key frames using shot boundary, namely Distance- Left-Right (DLR), is addressed, which is computed on a sliding sub-window basis. The DLR sequence is used to detect the suspected shot boundary frames and a transition type detection procedure is adapted to these suspected frames for discriminating the abrupt and gradual transitions. The proposed shot boundary detection methodology yields Precision—95.02%, Recall—93.15% and F1 score—94.07% for cut, Precision—86.57%, Recall—86.67% and F1 score—86.61% for gradual, Precision—90.6%, Recall—90.02% and F1 score—90.3% for overall transitions. Experimental results show that the proposed approach is superior to the recently available shot boundary detection techniques because of its robustness and simplicity, and presents an effective distance metric to detect the shot boundary.
机译:视频镜头边界检测是基于内容的视频管理和检索系统的主要任务。通过利用归一化周期图的优点来有效地表示视频内容,提出了镜头边界检测策略。提出了一种基于归一化周期图的距离度量,该距离度量使用镜头边界检测关键帧,即距离-左-右(DLR),该距离度量是在滑动子窗口的基础上计算的。 DLR序列用于检测可疑的镜头边界帧,并且过渡类型检测过程适用于这些可疑的帧,以区分突然的和逐渐的过渡。拟议的镜头边界检测方法可产生精确度-95.02%,召回率-93.15%和F1得分-切入,精确度-86.57%,召回率-86.67%和F1分数-渐进式,精确度-90.6%,召回率-总体转换率为90.02%,F1得分为90.3%。实验结果表明,该方法由于其鲁棒性和简单性而优于最近可用的镜头边界检测技术,并提出了一种有效的距离度量来检测镜头边界。

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