一种自动镜头聚类方法

镜头聚类是视频内容分析的重要途径。镜头聚类的基本任务是基于镜头的物理特征对镜头进行分类。本文设计和实现了一种新的镜头聚类方法 ,这种方法从一个初始分割开始 ,经多次聚类分裂与合并的迭代 ,自动地进行误差校正。这种方法既不需要通过人工交互来解决试探聚类方法的误差调节问题 ,也不需要迭代聚类算法中难以确定的经验参数和经验阈值的设定 ,克服了普通聚类方法的缺点 ,在实际应用系统中取得了较好的效果。

An Automatic Shot Clustering Method

Shot clustering is an important aspect of video content analysis. The basic task of shot clustering is to classify shots based on their low-level features. This paper describes a novel shot clustering technique. Beginning with an initial classification of the shot set, our algorithm proceeds with merging and splitting iteration alternatively to reduce the errors in the initial results. The main advantage of this algorithm is that it does not need any experiential parameters or thresholds, nor does it need any manual interaction. In this way,our algorithm overcomes shortcomings of tradi tional clustering algorithm and works well in practical systems.