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

长期以来，视频分析社区一直试图弥合从低级特征提取到语义理解和检索的鸿沟。阻碍进展的一个重要障碍是缺乏必要的基础设施来构建有用的语义概念本体，构建从视频中提取特征的模块，解释视频所包含内容的语义，以及根据基准真理数据评估任务。为了解决这一根本问题，该项目将围绕大型视频集合、提取的特征、视频分割工具、可扩展的带注释的语义概念词典、概念之间的本体关联、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，以及最终用于视频检索评估的基准用户查询集创建一个共享的社区资源。该资源将允许研究人员构建自己的图像/视频分类器，测试新的底层特征，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S的基础设施。使用该工具套件和参考实现，研究人员可以为不同的子域快速定制概念本体和分类器。该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。拟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。大量多样化的标准化视频数据集和定义明确的基准程序的使用确保了评估科学进步的严格过程。结果将促进新想法和解决方案的快速探索，有助于促进主要社会利益的进步，如下一代媒体搜索和安全。网址：http://www.informedia.cs.cmu.edu/analyticsLibrary

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