Semantic based adaptive movie summarisation

Abstract

This paper proposes a framework for automatic video summarization by exploiting internal and external textual descriptions. The web knowledge base Wikipedia is used as a middle media layer, which bridges the gap between general user descriptions and exact film subtitles. Latent Dirichlet Allocation (LDA) detects as well as matches the distribution of content topics in Wikipedia items and movie subtitles. A saliency based summarization system then selects perceptually attractive segments from each content topic for summary composition. The evaluation collection consists of six English movies and a high topic coverage is shown over official trails from the Internet Movie Database. © 2010 Springer-Verlag Berlin Heidelberg.