基于超图聚类的故事单元的抽取与分析

合理而有效地结构化组织视频数据是浏览、检索和管理视频的重要前提.提出了一种新的层次化的结构化组织视频数据的提取故事单元的方法.它采用k-近邻超图描述镜头间的内容相似关系,利用超图模型对镜头聚类,并通过分析镜头类别间的时间投影关系提取故事单元,并采用一维字符串描述故事单元.此外,还提出了结合领域知识确定故事单元的类型的算法框架,并将其用于对话场景的检测.将算法用于测试多段视频,实验结果令人满意.

Extraction and Analysis of Story Unit Based on Clustering of Hyper-Graph

It is the important precondition for browsing, retrieval and indexing videos that effectively and rationally organizing the video structure. In this paper, for hierarchically organizing the video structure, a method for extracting story units is proposed. k-nearest neighbor hyper-graph is used to represent the content relations among shots, and shots are clustered based on hyper-graph model. By analyzing time projection relations among shot clusters, story units are extracted, and represented by the 1D strings. A frame using specific domain knowledge for identifying the type of story unit is also proposed, and is applied to identify the dialogs in the videos. The new algorithm is applied to multiple test videos, and the experiments results are satisfying.