Communications of the ACM
Metrics for shot boundary detection in digital video sequences
Multimedia Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
A fuzzy logic method of feature representation for shot boundary detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel clustering algorithm based on variable precision rough-fuzzy sets
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
A novel feature weighted clustering algorithm based on rough sets for shot boundary detection
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
HUGVid: handling, indexing and querying of uncertain geo-tagged videos
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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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.