基于主动学习的视频对象提取方法  

Video object extraction method based on active learning SVM

作　　者：王学军[1] 赵琳琳[1] 王爽[1] 

机构地区：[1]吉林大学通信工程学院,长春130012

出　　处：《吉林大学学报（工学版）》2013年第S1期51-54,共4页Journal of Jilin University:Engineering and Technology Edition

基　　金：吉林省科技发展计划项目(20090514)

摘　　要：提出了一种基于主动学习的SVM视频对象提取方法。利用主动学习的思想,对传统的支持向量机进行了改进,将SVM和主动学习的优点结合起来,实现了更加准确提取视频对象的目的。通过自适应变化检测获取初始视频对象,并以其作为训练样本,选取正类样本训练支持向量机,构造加强的分界面。实验结果表明,该方法能克服一般SVM方法的缺点,使视频对象的边缘更加精确,同时减小了计算量。A video object extraction method based on active learning SVM was presented.By using the idea of active learning,the traditional SVM was improved,so the extracted video object was more accurately.An adaptive change detection method was used for obtaining the original video object,then the object was chosen for SVM training,and the positive samples were trained for constructing the intensifying classification plane.Experiment results show that the proposed method can overcome the shortage of the traditional SVM,get more exactly object edge,and reduce the computational complexity.

关 键 词：视频对象提取 支持向量机 自适应变化检测 主动学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]