Abstract

This paper proposes a novel approach and a new benchmark for video summarization. Thereby we focus on user videos, which are raw videos containing a set of interesting events. Our method starts by seg- menting the video by using a novel “superframe” segmentation, tailored to raw videos. Then, we estimate visual interestingness per superframe using a set of low-, mid- and high-level features. Based on this scoring, we select an optimal subset of superframes to create an informative and interesting summary. The introduced benchmark comes with multiple human created summaries, which were acquired in a controlled psycho- logical experiment. This data paves the way to evaluate summarization methods ob jectively and to get new insights in video summarization. When evaluating our method, we find that it generates high-quality re- sults, comparable to manual, human-created summaries.