镜头聚类是视频内容分析的重要途径。为能够自动、准确地实现镜头聚类，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，即能自动地进行误差校正，而且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，也不需要在迭代聚类算法中进行难以确定的经验参数和经验阈值的设定。实验证明，该方法能较好地解决镜头的自动、准确聚类问题。

Shot clustering is an important issue in the field of video content analysis.The basic task of shot clustering is to classify shots based on their low level physical 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 initial classification is based on the closeness of shots.The following clustering process is controlled by merging and splitting rules,which are based on the concepts of centroid,radius,intra cluster hole,and inter cluster space.The basic idea is that:(1)the intra cluster hole of a cluster should be less than the intercluster space; and (2)the centroid distance of two clusters should be larger than the sum of their radiuses.Special consideration is put on the design of the iteration mode to suppress the possible errors.The main advantage of this algorithm is that it does not need any experiential parameters or thresholds,neither does it need any manual interaction,which is a basic requirement for automatic clustering of shots with no domain knowledge.Experimental results are presented and analyzed.