A Video Indexing Ontology Using Fuzzy Metadata

使用模糊元数据的视频索引本体很难被机器理解。虽然人类有一种表面上看起来“直接”的方式来观察和理解背景、前景物体和运动，但视频理解一直是自动视频和图像分析中令人困惑的问题之一。这个项目提出了一种“分而治之”的方法，其中将使用数百个通用的概念(例如，户外、动物)来描述和注释视频中常见的大量场景。类似于人们可以在图书馆卡目录中找到的有限词汇表的索引词集，每个视频场景都可以通过这些概念的组合(“元数据”)进行注释。除了视频中可见的对象、动作和场景的简单列表外，精心选择的概念还允许描述它们之间的关系(“本体”)，这允许更丰富的复合描述。挑战将是定义这些概念，使它们同时满足几个标准：*这些概念必须代表在视频广播中频繁可见的事物。*这些概念必须是可清楚识别的，以便计算机算法有机会自动检测它们。*这些概念必须可链接到定义概念如何相关的本体中。由于视频注释，无论是由计算机还是人工完成的，总是包含错误，这项工作将把概率置信度度量(“模糊元数据”)纳入注释中。现有的索引和分类方案没有明确定义用于测量和报告索引注释的错误和遗漏的标准；因为文献管理员和档案管理员传统上认为索引只包含完整的、可信的和经验证的元数据。此外，该项目将评估这些概念可以在多大程度上利用最先进的视频分析技术自动提取。该项目将使用纪录片和电视新闻中的镜头，进行视频搜索和检索实验，以确定本体的有用性和注释的可信度。URL：http://www.informedia.cs.cmu.edu/ontology

A Video Indexing Ontology Using Fuzzy MetadataVideo has been very difficult to understand by a machine. While humanshave a seemingly "direct" way of seeing something and understanding ascene in terms of background, foreground objects, and motions, videounderstanding has been one of the perplexing problems of automatic videoand image analysis to date.This project proposes a "divide and conquer" approach, where severalhundred general-purpose concepts (e.g., outdoors, animals) will be usedto describe and annotate a very large universe of scenes commonlydepicted in video. Analogously to a limited vocabulary set of indexingterms one might find in a library card catalog, each video scene can beannotated through a combination of these concepts ("metadata"). To gobeyond a mere listing of objects, actions and scenes visible in thevideo, carefully chosen concepts also allow the description ofrelationships between them ("ontology"), which allows for much richercomposite descriptions. The challenge will be to define these conceptsso that they satisfy several criteria at once:* The concepts must represent things frequently visible in videobroadcasts.* The concepts must be clearly identifiable to give computeralgorithms a chance to detect them automatically.* The concepts must be linkable into an ontology that defines howconcepts are related.Since video annotations, whether done by a computer or a human, alwayswill contain errors, this work will incorporate probabilistic confidencemetrics ("fuzzy metadata") into the annotation. No existing indexing andclassification schemes have explicitly defined standards for measuringand reporting errors and omissions of indexing annotations; sincelibrarians and archivists have traditionally assumed that an indexcontains only complete, trusted and verified metadata.Furthermore, the project will assess to what extent these concepts canbe automatically extracted with state of the art video analysistechniques. Using footage from documentaries and television news, theproject will perform video search and retrieval experiments to determinethe usefulness of the ontology and the confidence of the annotations.URL: http://www.informedia.cs.cmu.edu/ontology