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。虽然人类在背景、前景物体和运动方面有一种看似“直接”的方式来观察和理解事物，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。该项目提出了一种“分而治之”的方法，其中数百个通用概念（例如，户外，动物）将被用来描述和注释一个非常大的宇宙的场景通常描绘在视频中。类似于图书馆卡片目录中有限的索引术语集，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。为了超越视频中可见的对象、动作和场景的简单列表，精心选择的概念还允许描述它们之间的关系（“本体论”），这允许更丰富的复合描述。挑战将是定义这些概念，以便它们同时满足几个标准：* 这些概念必须代表在视频广播中经常可见的事物。 这些概念必须是清晰可识别的，以便计算机算法有机会自动检测它们。 这些概念必须被嵌入到一个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类完成，总是会包含错误，这项工作将把概率置信度（“模糊元数据”）纳入注释。现有的索引和分类方案都没有明确定义标准来衡量和报告索引注释的错误和遗漏;因为图书管理员和档案管理员传统上认为索引只包含完整的、可信的和经过验证的元数据。此外，该项目将评估这些概念在多大程度上可以用最先进的视频分析技术自动提取。使用来自纪录片和电视新闻的片段，该项目将执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。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