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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Automatic Video Shot Boundary Detection Using k-means Clustering and Improved Adaptive Dual Threshold Comparison
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Automatic Video Shot Boundary Detection Using k-means Clustering and Improved Adaptive Dual Threshold Comparison

机译：使用k均值聚类和改进的自适应双阈值比较的自动视频镜头边界检测

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摘要

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.
机译：当前，基于内容的视频检索（CBVR）是最主流的视频检索方法，它利用自身的视频功能来执行自动识别和检索。该方法涉及关键技术，即镜头分割。提出了一种基于K均值聚类和改进的自适应双阈值比较的视频镜头边界自动检测方法。首先，提取每个帧的视觉特征，并使用K-means聚类算法将其分为两类，即一类发生重大变化，另一类发生重大变化。然后，针对分类结果，利用改进的自适应双阈值比较方法确定镜头的突变边界和渐变边界。最后，实现了视频镜头边界自动检测系统。

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