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。 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