Vers un Résumé Automatique de Séries Télévisées basé sur une Recherche Multimodale d'Histoires

Modern TV series have complex plots made of several intertwined stories following numerous characters. In this paper, we propose an approach for automatically detecting these stories in order to generate video summaries and we propose a visualization tool to have a quick and easy look at TV series. Based on automatic scene segmentation of each TV series episode (a scene is defined as temporally and spatially continuous and semantically coherent), scenes are clustered into stories, made of (non necessarily adjacent) semantically similar scenes. Visual, audio and text modalities are combined to achieve better scene segmentation and story detection performance. An extraction of salient scenes from stories is performed to create the summary. Experimentations are conducted on two TV series with different formats