Learning From Cross-Domain Media Streams for Event-of-Interest Discovery

Every day, vast amounts of data are uploaded to various social-sharing websites. Each social-sharing website has its own media dataset. Recently, mining media datasets has shown great potential for our daily lives, e.g., earthquake detection. Generally, different datasets have different characteristics. Combining different datasets is capable of achieving better performance than using any dataset independently, particularly if the datasets can compensate for each other. The resulting performance, however, depends on the fusion method. Effectively combining different datasets is challenging. As a solution to this challenge, this paper presents a generic two-stage framework for events of interest. Specifically, the first stage normalizes the contents of different datasets to make them comparable; then, the second stage combines the normalized contents for a ranked event list using graph-based algorithms. Practically, this paper unifies a flow-based media dataset and a check-in-based media dataset. Based on the precision for the top n events, the experimental results demonstrate that the proposed framework can achieve better performance in finding events associated with sports, local festivals, concerts, and exhibitions compared with a state-of-the-art approach that uses one dataset alone.