III-COR: Analysis and Display of Semantics of Structured and Unstructured Videos

III-COR：结构化和非结构化视频的语义分析和显示

• 批准号: 0713064
• 负责人: John Kender
• 金额: $ 43万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713064&HistoricalAwards=false
• 关键词: III; COR; Analysis; Display; Semantics

This project explores three new related approaches to making the indexing and retrieval of videos (1) more efficient by developing rapid feature selection, (2) more meaningful by devising measurably useful indexing ontologies, and (3) more humanly navigable by demonstrating integrated multimedia browsers, even when the videos are unedited.The first approach consists of heuristic adaptations of machine learning algorithms to the overly redundant data that is video. The second approach consists of using statistical tools to examine the descriptive tags that can be affixed to video segments, in order to measure and refine their quality. The third approach consists of exploiting the first two approaches to discover and display the weak structure that is latent even in the unedited videos of student presentations. The experimental research will be refined by continuing user studies, involving students from middle school, high school, college, and post-graduates.The results of this project will provide advances at the multiple intersections of computer vision, machine learning, data management, information retrieval, ontology design, user interface technology, and user studies, with possible applications in the wider areas of sensory retrieval in general.Broader impacts: We expect that the browser will enhance the effectiveness of undergraduate education by allowing accurate and rapid review of both instructor and student recorded presentations.We also expect that the underlying novel technologies will permit real-time analysis and access of more standard videos. The project Web site (http://www.cs.columbia.edu/~jrk/unstructured) will be used to disseminate resulting publications, open-source code, and instructions on how to obtain annotated video data sets.

本项目探索了三种新的相关方法，以使视频的索引和检索(1)通过开发快速的特征选择更有效率，(2)通过设计可测量的有用的索引本体更有意义，(3)通过演示集成的多媒体浏览器更人性化地导航，即使视频未编辑。第一种方法是机器学习算法的启发式调整，以适应过于冗余的视频数据。第二种方法包括使用统计工具来检查可以贴在视频片段上的描述性标签，以衡量和改进其质量。第三种方法包括利用前两种方法来发现和展示甚至在未编辑的学生演示视频中潜在的弱结构。实验研究将通过持续的用户研究来完善，包括初中、高中、大学和研究生。该项目的成果将在计算机视觉、机器学习、数据管理、信息检索、本体设计、用户界面技术和用户研究等多个交叉领域取得进展，并可能在更广泛的感官检索领域得到应用。更广泛的影响：我们期望该浏览器能够准确、快速地回顾教师和学生的演讲记录，从而提高本科教育的效率。我们还期望，潜在的新技术将允许实时分析和访问更多的标准视频。项目网站（http://www.cs.columbia.edu/~jrk/unstructured）将用于传播结果出版物、开源代码和关于如何获得带注释的视频数据集的说明。