Associative mining for intelligent organisation and analysis of multimedia information

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，用于高效组织、导航和分析的技术无法跟上步伐。部分问题是，媒体消费(浏览/分析)要好得多，方法是建立一种先入为主的概念，即什么是“有趣的”事件，并迫使媒体收藏围绕这一概念进行组织，从而促进快速和相关的访问。重组大型媒体收藏以实现非线性访问的问题并不新鲜，这为基于内容的检索和基于概念的检索提供了很大的推动力：这两个核心研究前沿支撑着当前视频搜索的最先进水平。然而，这样的成功高度依赖于注释，而且越来越明显的是，集合的增长迅速超出了我们可靠地进行注释的能力。迫切需要在没有先验知识的情况下自动组织多媒体内容的方法。这可以表现为无监督模式或事件中的问题，其中寻求媒体流的内在表示作为其重组的基础。鉴于视觉注意模型的最新进展，它考虑了引导人类视觉/听觉注意的自然队列和反应，我们问：是否可能在多媒体数据中存在某些内在的模式或不规则性，可以更恰当地反映自然映射到语义事件？具体地说，拟议的研究计划将通过调查自然的、自我组织的无监督学习方法与似乎显著引导人类兴趣的视觉注意机制之间的可能协同作用来解决事件发现问题。这项研究的一般范围将导致开发工具，以弥补广播新闻、体育、电视和电影制作、会议、个人生活日志、电子编年史、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测定方面的一系列紧迫缺陷。

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.