Segmenting with style: detecting program and story boundaries in TV news broadcast videos

Television news is an important medium to convey information to masses. This motivates several stakeholders to monitor and analyze the news broadcasts. Segmentation of streaming broadcast into programs and stories is a necessary first step for such analysis. Television news producers use predefined and unique presentation styles to create the channel content. Presentation styles vary with program and news story category, broadcast time, targeted audience etc. This motivated us to use presentation styles as features for segmenting news broadcasts. We propose a novel approach for characterization of spatio-temporal presentation styles. This involves characterization of spatial styles using a set of (presentation style specific) semantic shot categories derived from LSCOM-Lite Ontology. We also identify features and classifiers to automate the process of shot labeling for spatial style characterization. Further, the temporal presentation styles of shots are modeled using conditional random fields. This spatio-temporal modeling of presentation styles is used for segmenting the broadcast into programs and stories. We have also contributed a 360 hours broadcast video dataset acquired from three Indian English news channels with ground-truth marked semantic shot categories, program genres and story boundaries. Experimentations on this dataset have shown the utility of our proposal for news broadcast video segmentation.