视频复杂语义分析关键技术研究

项目名称： 视频复杂语义分析关键技术研究

项目编号： No.61272393

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 汪萌

作者单位： 合肥工业大学

项目金额： 81万元

中文摘要： 随着视频数据采集设备的普及和互联网技术的发展，视频数据量呈爆炸式增长。高效的视频语义分析技术成为迫切需求。然而，由于传统的视频分析研究通常集中于简单语义概念检测，这使得其难以处理用户的复杂需求，例如查询复杂的视频检索。本课题致力于研究视频复杂语义分析中的关键技术，提出通过层次化处理"语义鸿沟"来实现复杂语义对象的建模，并围绕技术路线中若干有待解决的问题开展研究。具体内容包括：对复杂语义对象进行分析，抽取简单语义概念并分析它们的时序关系；基于多信息源融合建立简单概念的高性能模型库；基于简单概念的模型，对语义概念群进行建模；对语义概念群序列建立基于概率分布距离的区分式模型；提出结合多种标定模式的人工交互方法以及结合多种样本选择准则的主动学习方法。本课题将有力推动视频分析的理论和应用，为新一代视频服务与管理提供核心算法与技术。

中文关键词： 视频检索；视频语义分析；视频概念检测；；

英文摘要： With rapid advances of video capture devices and Internet technology, we have witnessed an explosive growth of video data. Effective video semantic analysis techniques are highly desired. However, conventional research on video analysis usually focuses on the detection of simple concepts, and this makes the technology difficult to deal with users' complex requirements, such as complex-query video search. In this proposal, we introduce a framework for complex-semantics-oriented video analysis. We propose a method to build the models of complex semantic objectives by bridging the "semantic gap" with multiple layers. More specifically, we bridge the semantic gap between low-level features and complex semantic objectives with three types of mapping: (1) the mapping from low-level features to simple semantic concepts; (2) the mapping from simple semantic concepts to concept bundles; and (3) the mapping from concept bundles to concept bundle sequences. The solution involves a lot of novel techniques, including: the analysis and representation of complex semantic objectives; the modeling of a set of simple semantic concepts by aggregating multiple information sources; the modeling of semantic concept bundles and concept bundle sequences; a user interaction approach that supports multiple labeling paradigms and an activ

英文关键词： Video retrieval；Video Semantic Analysis；Video Concept Detection；；