摘要
视频本身具有一定的层次结构,不同层次会产生不同粒度的语义,而且不同粒度的语义之间会形成一定的层次结构。因此,视频语义提取和标注强调语义的结构化。为此,首先,以镜头为单位提取其语义,并组成镜头语义序列。随后,带有简单时序关系的镜头语义序列经过结构化支持向量机的分析将产生结构化的视频语义;最好,将连续且内容相关的镜头作为一个场景,以视频场景为基本单位利用决策树算法C4.5根据镜头的语义信息及镜头之间的结构信息完成场景语义的推理。
Video has a hierarchical structure which means that different semantic with different granularity emerge among different hierarchies. Hence,it is important to extract and annotate video semantic structurally. The method adopted in this paper is divided into three phrases. First,it extracts semantic from each shot and combines into a semantic sequence. Second,with simple time series information,Struct-SVM is employed to generate structural video semantics. Finally,after combining sequential and contented-associated shots into a scene,it employs C4.5 to reduce scene semantic with structural video semantics.
出处
《电脑知识与技术（过刊）》
2014年第9X期6178-6180,6196,共4页
Computer Knowledge and Technology
基金
福建省自然科学基金(2013J01231)
