Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration’s content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights on movies. We demonstrate this model on two very popular movies.
Content based browsing and navigation in digital video collections have been centered on sequential and linear presentation of images. To facilitate such applications, nonlinear and non sequential access into video documents is essential, especially with long programs. For many programs, this can be achieved by identifying underlying story structures which are reflected both by visual content and temporal organization of composing elements. A new framework of video analysis and associated techniques are proposed to automatically parse long programs, to extract story structures and identify story units. The proposed analysis and representation contribute to the extraction of scenes and story units, each representing a distinct locale or event, that cannot be achieved by shot boundary detection alone. Analysis is performed on MPEG compressed video and without a prior models. The result is a compact representation that serves as a summary of the story and allows hierarchical organization of video documents.