Associative mining for intelligent organisation and analysis of multimedia information

多媒体信息智能组织与分析的联想挖掘

• 批准号: 372077-2009
• 负责人: Kyan, Matthew
• 金额: $ 1.24万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=461516
• 关键词: Associative; mining; intelligent; organisation; analysis

The overwhelming amounts of multimedia information generated in today's media-centric society present a host of challenges to end-users, businesses and researchers alike: quite simply, technologies for efficient organization, navigation and analysis cannot keep pace. Part of the problem is that consumption of media (browsing/analysis) would be far better served by building a pre-conceived notion of what constitutes an "interesting" event, and forcing the media collection to be organized around that, thereby promoting fast and relevant access. The problem of restructuring large media collections for non-linear access is not new, having provided much impetus for both content- and concept-based retrieval: the two core research fronts underpinning current state-of-the-art in video search. Such successes, however, are highly dependent on annotation, and it is becoming increasingly evident that collections are fast outgrowing our abilities to reliably annotate. Methodologies are desperately needed to automatically organize multimedia contents without the benefit of prior knowledge. This can be cast as a problem in unsupervised pattern or event "discovery", wherein an intrinsic representation of a media stream is sought as a basis for its reorganization. In light of recent advances in visual attention modeling, which considers the natural queues and responses that direct human visual/aural attention, we ask: is it possible that there exist certain intrinsic patterns or irregularities in multimedia data, that can more appropriately reflect natural mappings to semantic events? Specifically, the proposed research program will address event discovery by investigating the possible synergies between natural, self-organizing approaches to unsupervised learning and the visual attentive mechanisms that appear to significantly guide human interest. The generic scope of this research will result in the development of tools to serve a range of pressing deficiencies in the summarization, navigation and consumption of broadcast news, sports, TV and film production, meetings, personal lifelogs, eChronicles, media-centric management systems and PVR's, security and unmanned surveillance, biomedical image informatics and biometrics.

在当今以媒体为中心的社会中产生的大量多媒体信息给最终用户、企业和研究人员带来了一系列挑战：很简单，用于有效组织、导航和分析的技术无法跟上。问题的一部分是，媒体的消费（浏览/分析）将更好地服务于建立一个什么是“有趣的”事件的先入为主的概念，并迫使媒体收藏围绕这一点组织，从而促进快速和相关的访问。为非线性访问重组大型媒体收藏的问题并不新鲜，为基于内容和基于概念的检索提供了很大的动力：这两个核心研究前沿支撑着当前最先进的视频搜索。然而，这样的成功高度依赖于注释，并且越来越明显的是，集合的快速增长超出了我们可靠注释的能力。 迫切需要一种方法来自动组织多媒体内容，而无需先验知识。这可以被视为无监督模式或事件“发现”中的问题，其中，寻求媒体流的固有表示作为其重组的基础。 鉴于视觉注意力建模的最新进展，它认为自然队列和响应，直接人类的视觉/听觉注意力，我们问：是否有可能存在某些内在的模式或不规则的多媒体数据，可以更恰当地反映自然映射到语义事件？ 具体来说，拟议的研究计划将通过调查自然的、自组织的无监督学习方法与似乎能显著引导人类兴趣的视觉注意机制之间可能的协同作用来解决事件发现问题。这项研究的通用范围将导致工具的开发，以服务于广播新闻，体育，电视和电影制作，会议，个人生活日志，eChronicles，以媒体为中心的管理系统和PVR，安全和无人监控，生物医学图像信息学和生物识别技术的总结，导航和消费的一系列紧迫的缺陷。