Adaptive video shot detection improved by fusion of dissimilarity measures
Due to the large amount of videos generated through several data sources, the development of efficient mechanisms for storing, indexing, retrieving and visualizing their content is a challenging task. Temporal video segmentation is the automatic process of detecting transitions in video sequences, which is a fundamental step in the analysis of video content. This work proposes and evaluates an improved shot detection method based on the fusion of multiple frame dissimilarity measures and an adaptive threshold strategy. Experimental results demonstrate that the combination of different temporal features associated with an adequate threshold estimation can substantially improve the performance of individual methods.