压缩域中基于支持向量机的镜头边界检测算法

针对如何进一步提高镜头边界检测精度问题,本文提出了一个基于支持向量机SVM (Support Vector Machine)的镜头边界检测算法.该算法利用视频压缩域中特征,如宏块类型,帧间对应宏块DC系数差和帧类型将视频帧分为发生切变的帧、发生渐变的帧和非镜头变换帧三类,从而实现视频的镜头分割.实验结果表明该算法对摄像机的运动和大物体的进入具有很好的鲁棒性,且没有大多数算法中阈值选择的困难,将我们的算法与2001 TREC评估中最佳指标进行了比较,在综合度量查全率和查准率的性能指标F1上,比2001 TREC评估中最佳指标高约8％.

Algorithm for Shot Boundary Detection Based on Support Vector Machine in Compressed Domain

Improving the precision of shot boundary detection is very important.This paper presents an algorithm for shot boundary detection based on SVM(support vector machine)in compressed domain.It uses the features,such as the type of macroblock,the difference between DC coefficients of two co-located blocks in successive frames and the type of frame,to segment a video into the shots by classifying the frames into three classes,namely,the frames of cut change,gradual change and non-change.No thresholds,which are often hard to select in most shot detection methods,are involved in our algorithm.Experiments have shown that our algorithm is robust for motion of camera and large objects in video,and the experimental result of our algorithm on TREC-2001 video data set is 8% higher than the best result of 2001 TREC evaluation in F1 comparison when cut and gradual changes are both considered.