Video knowledge augmentation based on summarized contents and online media

Exploration techniques of video knowledge have been proposed for years to help people discover the details about videos. However, existing systems still yield limited information for users. In this paper, we present a video knowledge browsing system, which can establish the framework of a video based on its summarized contents and expand them by using online correlated media. Thus, users can not only browse key points of a video efficiently but also focus on what they are interested in. In order to construct the fundamental system, we make use of our previous proposed approaches to transforming a video into a graph. After the relational graph is built up, the social network analysis is then performed to explore online relevant resources. We also apply the Markov clustering algorithm to enhance the results of the network analysis. The experiments demonstrate that our system can achieve better performance than the traditional systems.