Adaptive clustering and interactive visualizations to support the selection of video clips

Although people are capturing more video with their mobile phones, digital cameras, and other devices, they rarely watch all that video. More commonly, users extract a still image from the video to print or a short clip to share with others. We created a novel interface for browsing through a video keyframe hierarchy to find frames or clips. The interface is shown to be more efficient than scrolling linearly through all keyframes. We developed algorithms for selecting quality keyframes and for clustering keyframes hierarchically. At each level of the hierarchy, a single representative keyframe from each cluster is shown. Users can drill down into the most promising cluster and view representative keyframes for the sub-clusters. Our clustering algorithms optimize for short navigation paths to the desired keyframe. A single keyframe is located using a non-temporal clustering algorithm. A video clip is located using one of two temporal clustering algorithms. We evaluated the clustering algorithms using a simulated search task. User feedback provided us with valuable suggestions for improvements to our system.