Topic mining on web-shared videos
Internet videos have grown exponentially with the help from video sharing Websites. Automatic topic mining is therefore increasingly important for organizing and navigating such large video databases. Most of current solutions of topic detection and mining were done on news videos and cannot be directly applied on Web videos, because of their limited and noisy semantic information. In this paper, we will try to address this problem and propose an automatic topic mining framework on Web videos. We develop an iterative weight-updated co-clustering scheme to filter "noisy" tags and mine the "hot" topics. We then propose a visual-based clustering approach to further group the videos with similar content, and rank the visual-similar groups by their similarity to the topic center. Experiments on a large Web video database demonstrate the superior performance of our weight-updated co-clustering to both of the traditional co-clustering and k-means. The experiments also demonstrate significant improvement of users' experience by our visual-based clustering and ranking.