Event driven summarization for web videos

The explosive growth of web videos brings out the challenge of how to efficiently browse hundreds or even thousands of videos at a glance. Given an event-driven query, social me-dia web sites can easily return a ranked list of large but diverse and somewhat noisy videos. Users often need to painstakingly explore the retrieved list for an overview of the event. This paper presents a novel solution by mining and threading ”key ” shots, which can provide an overview of main contents of videos at a glance, by summarizing a large set of diverse videos. The proposed framework com-prises three stages for multi-video summarization. Firstly, given an event query, a ranked list of web videos together with their associated tags are retrieved. Key shots are then established by near-duplicate keyframe detection, ranked ac-cording to informativeness and threaded in a chronological order. Finally, summarization is formulated as an optimiza-tion procedure which trades off between relevance of key shots and user-defined skimming ratio. The framework pro-vides the summary with the way of dynamic video skim-ming. We conduct user studies on twelve event queries for over hundred hours of videos crawled from YouTube. The evaluation demonstrates the feasibility and effectiveness of the proposed solution.