Adaptive Clustering and Interactive Visualizations to Support the Selection of Video Clips

Abstract

User-generated video from mobile phones, digital cameras, and other devices is increasing, yet people rarely want to watch all the captured video. More commonly, users want a single still image for printing or a short clip from the video for creating a panorama or for sharing. Our interface aims to help users search through video for these images or clips in a more efficient fashion than fast-forwarding or “scrubbing” through a video by dragging through locations on a slider. It is based on a hierarchical structure of keyframes in the video, and combines a novel user interface design for browsing a video segment tree with new algorithms for keyframe selection, segment identification, and clustering. These algorithms take into account the need for quality keyframes and balance the desire for short navigation paths and similarity-based clusters. Our user interface presents keyframe hierarchies and displays visual cues for keeping the user oriented while browsing the video. The system adapts to the task by using a non-temporal clustering algorithm when a the user wants a single image. When the user wants a video clip, the system selects one of two temporal clustering algorithm based on a measure of the repetitiveness of the video. User feedback provided us with valuable suggestions for improvements to our system.