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功能提取到语义理解和检索的差距。阻碍进步的一个重要障碍是缺乏构建有用的语义概念本体的基础架构，构建模块以从视频中提取功能，解释视频所包含内容的语义，并评估针对基准真实数据的任务。为了解决这个基本问题，该项目将围绕大型视频收集，提取的功能，视频分割工具，具有注释的可扩展语义概念词典，彼此相关的本体论，用于注释的工具，学习模型和完整的软件模块，以通过概念进行自动构建视频，并允许用户质疑的视频设置来构建视频，并将其与Quarties reforiection构建。分类器，测试新的低水平功能，扩展概念本体，并探索更高级别的搜索服务等，而无需重新开发数年的基础架构的价值。使用此工具套件和参考实施，研究人员可以快速自定义概念本体和分类器来进行多样化的子公司，以进行多样化的搜索范围，以促进拟议的搜索范围，以进行大量的研究和数量的研究。拟议资源的模块化体系结构在添加新的本体论和测试其他研究人员开发的新分析组件方面具有极大的灵活性。使用大量不同的标准化视频数据集和定义明确的基准程序可确保严格评估科学进步的过程。结果将有助于快速探索新的思想和解决方案，从而有助于主要社会兴趣的进步，例如下一代媒体搜索和安全性。