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.