A Shot Boundary Detection Method for News Video Based on Rough Sets and Fuzzy Clustering

With the rapid growing amount of multimedia, content-based infomation retrieval has become more and more important. As a crucial step in 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 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 on more than 2 h of news programs and 98.0% recall with 96.6% precision have been achieved.