HCC: Medium: Collaborative Research: Computer Vision and Online Communities: A Symbiosis

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目代表了计算机视觉研究的一个新的范式，与基于内容的图像管理有关？它利用了这项技术和它所服务的在线社区之间的共生关系。很明显，社区将从视觉技术支持的改进的搜索和浏览工具中受益，但我们认为匡威也是如此。在线共享的视觉信息几乎总是嵌入在社交网络中，这些网络提供了丰富的上下文信息，这对视觉系统来说是一个布恩。我们的目标是为“社会意识”的新一代计算机视觉系统奠定基础。这些视觉系统将利用嵌入式在线社区中发生的个人和社会事件之间的关系的知识，并且在这样做的过程中，它们将比当前技术表现出显着的性能提升。他们将使用现有在线社区中嵌入的数据进行有区别的培训，并根据社区成员如何使用他们的观察进行调整。几乎每一张在网上分享的图像都嵌入了某种形式的在线社区，任何试图分析和分类这些图像的系统都将从拟议的研究中获益。我们预见到希望共享历史图像档案、科学图像集、法医图像、监控视频等的社区将从中受益。此外，当我们专注于搜索和浏览图像的工具时，我们的工作将对提高文本元数据的质量和密度产生重要影响。通过这种方式，这项研究将使在线社区的所有成员受益，包括那些有视觉障碍的人。

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.