Scene-based Scalable Video Summarization

Abstract

A scalable video summarization and navigation system is proposed in this work. Particularly, given the desired number of keyframes for a video sequence, we first distribute it among underlying video scenes and sinks based on their respective importance ranks. Then, we select the most important shot of each sink as its R-shot and further assign each sink's designated number of keyframes to its R-shot. Finally, a time-constrained keyframe extraction scheme is developed to locate all keyframes. Consequently, we can achieve a scalable video summary from the initial keyframe set by exploiting such a video structure-based ranking scheme. In addition, a content navigation tool is also developed which could help users freely access or locate specific video scenes or shots. Sophisticated user studies have shown that this summarization and navigation system can not only help users quickly browse video content, but also assist them in searching for particular video segments.