Extracting story units from long programs for video browsing and navigation

TLDR

Content‑based video browsing has focused on linear presentation, yet nonlinear, non‑sequential access is essential for long programs, which can be enabled by identifying underlying story structures reflected in visual content and temporal organization. The authors propose a framework to automatically parse long programs and extract story structures and units. The analysis operates on MPEG‑compressed video without prior models, employing the proposed techniques. The method produces a compact representation that summarizes the story and supports hierarchical organization of video documents, extracting scenes and units beyond shot‑boundary detection.

Abstract

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.