Making a scene: alignment of complete sets of clips based on pairwise audio match

As the amount of social video content captured at physical-world events, and shared online, is rapidly increasing, there is a growing need for robust methods for organization and presentation of the captured content. In this work, we significantly extend prior work that examined automatic detection of videos from events that were captured at the same time, i.e. "overlapping". We go beyond finding pairwise matches between video clips and describe the construction of scenes, or sets of multiple overlapping videos, each scene presenting a coherent moment in the event. We test multiple strategies for scene construction, using a greedy algorithm to create a mapping of videos into scenes, and a clustering refinement step to increase the precision of each scene. We evaluate the strategies in multiple settings and show that a greedy and clustering approach results in best possible balance between recall and precision for all settings.