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VIDEO SHOT BOUNDARY DETECTION USING GENERALIZED EIGENVALUE DECOMPOSITION AND GAUSSIAN TRANSITION DETECTION

机译:基于广义特征值分解和高斯变换检测的视频拍摄边界检测

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摘要

Shot boundary detection is the first step of the video analysis, summarization and retrieval. In this paper, we propose a novel shot boundary detection algorithm using Generalized Eigenvalue Decomposition (GED) and modeling of gradual transitions by Gaussian functions. Especially, we focus on the challenges of detecting the gradual shots and extracting appropriate spatio-temporal features, which have effects on the ability of algorithm to detect shot boundaries efficiently. We derive a theorem that discuss about some new features of GED which could be used in the video processing algorithms. Our innovative explanation utilizes this theorem in the defining of new distance metric in Eigen space for comparing video frames. The distance function has abrupt changes in hard cut transitions and semi-Gaussian behavior in gradual transitions. The algorithm detects the transitions by analyzing this distance function. Finally we report the experimental results using large-scale test sets provided by the TRECVID 2006 which has evaluations for hard cut and gradual shot boundary detection.
机译:镜头边界检测是视频分析,摘要和检索的第一步。在本文中,我们提出了一种新的镜头边界检测算法,该算法使用广义特征值分解(GED)并通过高斯函数对渐变过渡进行建模。特别是,我们着重于检测渐进镜头和提取适当的时空特征的挑战,这些挑战会影响算法有效检测镜头边界的能力。我们推导出一个定理,该定理讨论了可以在视频处理算法中使用的GED的一些新功能。我们的创新解释利用该定理定义了特征空间中用于比较视频帧的新距离度量。距离函数在硬切过渡中具有突变,而在渐变过渡中则具有半高斯行为。该算法通过分析该距离函数来检测过渡。最后,我们使用TRECVID 2006提供的大规模测试集报告了实验结果,该测试集对硬切和渐进式镜头边界检测进行了评估。

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