This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The project represents a new paradigm for research in computer vision related to content-based image management--?one that exploits a symbiosis between this technology and the online communities it serves. It is quite clear that communities stand to benefit from improved searching and browsing tools enabled by visual technology, but we argue that the converse is equally true. Visual information that is shared online is almost always embedded in a social network, and these networks provide rich contextual information that can be a boon to vision systems.
Our goal is to build foundations for a new generation of computer vision systems that are 'socially aware'. These vision systems will exploit knowledge of the relationships between individuals and the social events that occur in the embedding online community, and in doing so they will exhibit significant performance gains over current technology. They will be trained discriminatively using the data embedded in existing online communities, and they will adapt in response to observations of how they are used by community members.
Almost every image that is shared online is embedded in some form of online community, and any system that seeks to analyze and catalog such imagery will gain from the proposed research. We foresee benefits for communities that wish to share historical image archives, scientific image collections, forensic imagery, surveillance videos, and more. Also, while we focus on tools for searching and browsing imagery, our work will have the important effect of improving the quality and density of textual metadata. In this way, the research stands to benefit all members of an online community, including those with visual impairments.
