Complex event processing for content-based text, image, and video retrieval

Elizabeth K. Bowman, B. Broome, V. M. Holland, Douglas Summers-Stay, R. Rao, John Duselis, J. Howe, B. Madahar, A. Boury-Brisset, Bruce Forrester, P. Kwantes, G. Burghouts, J. V. van Huis, Adem Yasar Mulayim
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Abstract

This report summarizes the findings of an exploratory team of the North Atlantic Treaty Organization (NATO) Information Systems Technology panel into Content-Based Analytics (CBA). The team carried out a technical review into the current status of theoretical and practical developments of methods, tools and techniques supporting joint exploitation of multimedia data sources. In particular, content-based information retrieval and analytics was considered as a means to allow military experts to exploit multiple data sources in a rapid fashion for sensemaking and knowledge generation. Elements included contextual understanding of complex events through computational/human processing techniques, event prediction through the automated extraction of network features, temporal trends, hidden clusters and resource flows, and the use of machine processing for automated translation, parsing, information extraction, and summarization of unstructured and semistructured data. The main conclusions of the study are that important research gaps exist in all the technical areas covered in this report. Though the research areas and developments are being advanced in the military sector and the civil sector, in particular, they remain at low levels of technical maturity for defense and security system applications. It is recommended that NATO collaborative research effort be expanded to advance those approaches that are most pertinent to our overall aim of enhancing the contextual understanding of complex events through CBA of heterogeneous multimedia streams.
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用于基于内容的文本、图像和视频检索的复杂事件处理
本报告总结了北大西洋公约组织(NATO)信息系统技术小组的一个探索小组对基于内容的分析(CBA)的研究结果。该小组对支持联合利用多媒体数据源的方法、工具和技术的理论和实际发展现状进行了技术审查。特别是,基于内容的信息检索和分析被认为是一种手段,使军事专家能够以快速的方式利用多个数据源进行意义分析和知识生成。要素包括通过计算/人工处理技术对复杂事件的上下文理解,通过自动提取网络特征、时间趋势、隐藏集群和资源流来进行事件预测,以及使用机器处理对非结构化和半结构化数据进行自动翻译、解析、信息提取和总结。该研究的主要结论是,本报告所涵盖的所有技术领域都存在重要的研究差距。尽管在军事部门和民用部门的研究领域和发展正在取得进展,但它们在国防和安全系统应用方面的技术成熟度仍然很低。建议北约扩大合作研究工作,以推进那些与我们通过异构多媒体流的CBA增强对复杂事件的上下文理解这一总体目标最相关的方法。
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