为了将视频分割成镜头,目前的方法都是提取某些特征然后构造不同的相异性函数。然而,太多的特征就会降低镜头分割算法的效率。因此,有必要对每一个镜头检测决策进行特征约简。基于此,提出了基于粗糙集和模糊聚类的分类方法并得到了相应的决策规则。针对新闻场景的特殊性,将镜头分割成突变过渡、渐变过渡以及无场景变化3类。用超过2个小时的新闻视频所做的实验获得了96.5%的查全率和97.9%的准确率。

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.