A Shot Boundary Detection Method for News Video Based on Rough Sets and Fuzzy Clustering
基于粗糙集和模糊聚类的新闻视频镜头边界检测方法

As a crucial step in the content-based news video indexing and retrieval system,shot boundary detection attracts much more research interests in recent years.To partition news video into shots,many metrics were constructed to measure the similarity among video frames based on all the available video features.However,too many features will reduce the efficiency of the shot boundary detection.Therefore,it is necessary to perform feature reduction for every decision of the shot boundary.For this purpose,the classification method based on rough sets and fuzzy c-means clustering for feature reduction and rule generation is proposed.According to the particularity of news scenes,shot transition can be divided into three types: cut transition,gradual transition and no transition.The efficacy of the proposed method is extensively tested with news programs over 2 hours and 96.5% recall with 97.9% precision have been achieved.