基于概率超图的视频事件语义检测

摘要

The categorization of video events is blur and uncertain. Expanding the hyper-graph's point-set matrix into the probabilistic form of soft hyper-graph, and analyzing the correlation of events and semantic, the precision and recall rate of the multi-event retrieval system will be improved. This paper proposed a video event semantic detection algorithm based on probabilistic hyper-graph model (PHVESD). Firstly, the method integrated the low-level visual features, color, gray-level co-occurrence matrix, Tchebichef moment and Local Binary Pattern ( LBP);then defined the intimacy function of video segmentation and used the intimacy information construct probabilistic hyper-graph model, in which each hyper-edge corresponded to an event semantics;adopted Markov random walk model to predict the probability of video segment belonging to each hyper-edge;finally, classified the unknown video event with MAP. The authors used this method on traffic emergent multi-semantic events detection and compared the proposed method to another. The experimental results show that, compared to Multi Label Random Walk (MLRW) algorithm based on the hyper-graph model, the proposed method makes the multi-semantic event detection precision improve by 10%, the recall rate increase by 8%.%视频事件类别的归属具有模糊性和不确定性,将超图的点边射入矩阵拓展成概率形式的软超图进行关联关系分析和语义分析,将会更有利于提高多事件检索检测的精准率和召回率.提出基于概率超图模型的视频事件语义检测算法(PHVESD).该方法首先将颜色、灰度共生矩阵、Tchebichef矩、局部二值模式(LBP)等四种底层视觉特征进行融合；然后定义视频段的亲密度函数并利用亲密度的信息构建概率超图模型,其中每条超边对应一种事件语义；采用随机游走过程来预测视频段属于每条超边的概率；最后结合阈值采用条件概率模型对视频段进行事件语义分类.将该方法用于交通突发事件多语义检测中并与其他的识别算法相比较,实验结果表明,与基于超图模型的多标签随机游走算法(MLRW)相比,PHVESD的算法使多语义事件检测的准确率提高了10％,召回率提高了8％.