detecting events by clustering videos from large media databases
As the number of user-generated videos rises in the Internet, there is a growing need for more efficient search tools that enable the users to find the desired content. Moreover, the associated video metadata for the content is often incomplete or even misleading. This paper addresses the problem of finding events by utilizing the video metadata from a video database by proposing two novel methods that are used in parallel. The first one is missing data compensation, which harvests missing data values from the textual descriptions in the video metadata. The second one is a layered clustering method that divides the videos in the database into clusters, each of which is considered as an event. The methods are tested with manually selected data from YouTube. The results show that missing data compensation yields better results in terms of accuracy than using ram data, and that the clustering method provides acceptable results and is a promising approach for further research.
media database missing data compensation layered clustering event detection video clustering