CRI: CRD: Collaborative Research: Large Analytics Library and Scalable Concept Ontology for Multimedia Research

CRI：CRD：协作研究：用于多媒体研究的大型分析库和可扩展概念本体

• 批准号: 0751078
• 负责人: Shih-Fu Chang
• 金额: $ 28.5万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0751078&HistoricalAwards=false
• 关键词: CRI; CRD; Collaborative; Research; Large

The video analysis community has long attempted to bridge the gap from low-level feature extraction to semantic understanding and retrieval. One important barrier impeding progress is the lack of infrastructure needed to construct useful semantic concept ontologies, building modules to extract features from the video, interpreting the semantics of what the video contains, and evaluating the tasks against benchmark truth data. To solve this fundamental problem, this project will create a shared community resource around large video collections, extracted features, video segmentation tools, scalable semantic concept lexicons with annotations, ontologies relating the concepts to each other, tools for annotation, learned models and complete software modules for automatically describing the video through concepts, and finally a benchmark set of user queries for video retrieval evaluation.The resource will allow researchers to build their own image/video classifiers, test new low-level features, expand the concept ontology, and explore higher level search services, etc., without having to redevelop several person year?s worth of infrastructure.Using this tool suite and reference implementation, researchers can quickly customize concept ontologies and classifiers for diverse subdomains.The contribution of the proposed work lies in the development of a large number of critical research resources for digital video analysis and searching. The modular architecture of the proposed resources provides great flexibility in adding new ontologies and testing new analytics components developed by other researchers in different domains. The use of large diverse standardized video datasets and well-defined benchmark procedures ensures a rigorous process to assess scientific progress.The results will facilitate rapid exploration of new ideas and solutions, contributing to advancements of major societal interest, such as next-generation media search and security.URL: http://www.informedia.cs.cmu.edu/analyticsLibrary

视频分析社区长期以来一直试图弥合从低级特征提取到语义理解和检索的差距。阻碍进展的一个重要障碍是缺乏构建有用的语义概念本体所需的基础设施，构建模块以从视频中提取特征，解释视频包含的语义以及根据基准真实数据评估任务。为了解决这个基本问题，该项目将围绕大型视频集合、提取的特征、视频分割工具、可扩展的带有注释的语义概念词典、相互关联的概念本体、注释工具、学习模型和通过概念自动描述视频的完整软件模块创建一个共享社区资源，最后是一个用于视频检索评估的用户查询基准集。该资源将允许研究人员构建自己的图像/视频分类器，测试新的低级特征，扩展概念本体，探索更高级别的搜索服务等，而无需重新开发几个人年？基础设施的价值。使用该工具套件和参考实现，研究人员可以快速定制不同子领域的概念本体和分类器。所提出的工作的贡献在于开发了大量用于数字视频分析和搜索的关键研究资源。所建议资源的模块化架构在添加新本体和测试由不同领域的其他研究人员开发的新分析组件方面提供了极大的灵活性。使用大量不同的标准化视频数据集和定义良好的基准程序确保了评估科学进展的严格过程。研究结果将促进新想法和解决方案的快速探索，为下一代媒体搜索和安全等重大社会利益的进步做出贡献。URL: http://www.informedia.cs.cmu.edu/analyticsLibrary