Using association rule mining to enrich semantic concepts for video retrieval

In order to achieve true content-based information retrieval on video we should analyse and index video with high-level semantic concepts in addition to using user-generated tags and structured metadata like title, date, etc. However the range of such high-level semantic concepts, detected either manually or automatically, usually limited compared to the richness of information content in video and the potential vocabulary of available concepts for indexing. Even though there is work to improve the performance of individual concept classifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences are available to us. We describe in this paper our method for using association rule mining to automatically enrich the representation of video content through a set of semantic concepts based on concept co-occurrence patterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts of the LSCOM ontology. The evaluation of our results shows the usefulness of our approach

Enhancing Social Sharing of Videos: Fragment, Annotate, Enrich, and Share

Media consumption is an inherently social activity, serving to communicate ideas and emotions across both small- and large-scale communities. The migration of the media experience to personal computers retains social viewing, but typically only via a non-social, strictly personal interface. This paper presents an architecture and implementation for media content selection, content (re)organization, and content sharing within a user community that is heterogeneous in terms of both participants and devices. In addition, our application allows the user to enrich the content as a differentiated personalization activity targeted to his/her peer-group. We describe the goals, architecture and implementation of our system in this paper. In order to validate our results, we also present results from two user studies involving disjoint sets of test participants