基于朴素贝叶斯模型的新闻故事分割方法  

News Story Segmentation Method Based on Nave Bayes Model

作　　者：彭天强[1] 李弼程[1] 

机构地区：[1]解放军信息工程大学信息工程学院信息科学系,郑州450002

出　　处：《计算机工程》2009年第20期178-180,共3页Computer Engineering

摘　　要：提出一种基于朴素贝叶斯模型的新闻视频故事分割方法。通过对新闻视频进行镜头检测,获得候选故事边界点,从候选边界点周围镜头提取多模态中级特征,形成属性集合作为输入,应用朴素贝叶斯模型对候选边界点进行分类后对结果进行后处理,得到新闻故事。实验结果表明,该方法获得了较高的查准率和查全率,对不同类型的新闻节目有良好的适应性。This paper proposes a news story segmentation method based on Nave Bayes model.It gets the candidate boundary points through shot detection,extracts multi-modal middle-level features at the boundary points such as visual,audio type,motion and caption to generate a feature set as the input of the model,and uses trained Nave Bayes model to classify the candidate boundary points.After post-processing upon the result,the non-news parts are removed and the news parts are saved.Experimental results show that the method is effective and adaptive to the different kinds of news programs, and it achieves satisfactory precision and recall.

关 键 词：故事分割 朴素贝叶斯模型 多模态融合 中级特征 视频检索 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]