Next Generation TV through Automatic Multimedia Annotation Systems: A Hybrid Approach

After the advent of smartphones, it is time for television to see its next big evolution, to become smart TVs. But to provide a richer television user experience, multimedia content first has to be enriched. In recent years, the evolution of technology has facilitated the way to take and store multimedia assets, like photographs or videos. This causes an increased difficulty in multimedia resources retrieval, mainly because of the lack of methods that handle non-textual features, both in annotation systems and search engines. Moreover, multimedia sharing websites like Flickr or YouTube, in addition to information provided by Wikipedia, offer a tremendous source of knowledge interesting to be explored. In this position paper, we address the automatic multimedia annotation issue, by proposing a hybrid system approach. We want to use unsupervised methods to find relationships between multimedia elements, referred as hidden topics, and then take advantage of social knowledge to label these resulting relationships. Resulting enriched multimedia content will allow to bring new user experience possibilities to the next generation television, allowing for instance the creation of recommender systems that merge this information with user profiles and behavior analysis