Fast Content-Based Mining of Web2.0 Videos

Abstract

Abstract. The accumulation of many transformed versions of the same original videos on Web2.0 sites has a negative impact on the quality of the results presented to the users and on the management of content by the provider. An automatic identification of such content links between video sequences can address these difficulties. We put forward a fast solution to this video mining problem, relying on a compact keyframe descriptor and an adapted indexing solution. Two versions are developed, an off-line one for mining large databases and an online one to quickly post-process the results of keyword-based interactive queries. After demonstrating the reliability of the method on a ground truth, the scalability on a database of 10,000 hours of video and the speed on 3 interactive queries, some results obtained on Web2.0 content are illustrated.