There are two trends that have become obvious over the last two years. One is the growth in consumption of Internet video, and the other is the significant increase in smart phone ownerships, leading to increased growth and use of mobile Internet and mobile video. While the above trends have been having significant impact on the industry and consumers alike, we believe there is a third trend, though at its infancy, which will emerge and provide new opportunities and challenges in the coming years: personalized and interactive video content and services. Examples of such services that we already see today are personalized video recommendations, and personalized advertisements. We believe there will be more innovative services, which will make use of location information, and personal preferences, to provide unique and relevant experiences in the future.
The broad goal of this project is to develop ''enabling technologies'' which will allow personalization of content based on user preferences. At the heart of our approach is to automatically classify and categorize Internet video, such that we can perform user preference and intent analysis based on the videos viewed by the users. In order to make the solution scalable and applicable to the billions of Internet video views of millions of Internet users, we propose to develop completely automated video classification techniques that make use of the contextual information accompanying most Internet videos, as opposed to computationally expensive video classification techniques based primarily on audio or video processing.