Video Summarization by Learning Submodular Mixtures of Objectives

We present a novel method for summarizing raw, casu-ally captured videos. The objective is to create a short sum-mary that still conveys the story. It should thus be both, interesting and representative for the input video. Previous methods often used simplified assumptions and only opti-mized for one of these goals. Alternatively, they used hand-defined objectives that were optimized sequentially by mak-ing consecutive hard decisions. This limits their use to a particular setting. Instead, we introduce a new method that (i) uses a supervised approach in order to learn the im-portance of global characteristics of a summary and (ii) jointly optimizes for multiple objectives and thus creates summaries that posses multiple properties of a good sum-mary. Experiments on two challenging and very diverse datasets demonstrate the effectiveness of our method, where we outperform or match current state-of-the-art. 1