Comparison of Visual Features and Fusion Techniques in Automatic Detection of Concepts from News Video

This study describes experiments on automatic detection of semantic concepts, which are textual descriptions about the digital video content. The concepts can be further used in content-based categorization and access of digital video repositories. Temporal gradient correlograms, temporal color correlograms and motion activity low-level features are extracted from the dynamic visual content of a video shot. Semantic concepts are detected with an expeditious method that is based on the selection of small positive example sets and computational low-level feature similarities between video shots. Detectors using several feature and fusion operator configurations are tested in 60-hour news video database from TRECVID 2003 benchmark. Results show that the feature fusion based on ranked lists gives better detection performance than fusion of normalized low-level feature spaces distances. Best performance was obtained by pre-validating the configurations of features and rank fusion operators. Results also show that minimum rank fusion of temporal color and structure provides comparable performance