一种融合分层信息熵的MRF视频运动前景分割算法

A Multi-Layer MRF Model Fusing Entropy Information for Foreground Segmentation in Video Sequences

针对视频图像中相邻像素的相关性对前景分割的影响问题,提出了一种以熵图像为纽带的分层马尔可夫随机场(MRF)视频运动前景分割算法.通过图像像素层和信息层构建自适应像素模型和动态光滑模型,增强了视频图像中邻域像素的空间一致性和时间连续性.然后在马尔可夫模型的框架下,采用多环置信度传播算法求解最大后验概率估计,提高视频运动前景分割的质量.实验结果表明该方法能够在不同的视频图像序列条件下完成对运动前景的有效分割.

To deal with the problem of modeling pixel-pair relationship for foreground segmentation in video sequences,we propose a multi-layer Markov random field（MRF） model fusing entropy information.Pixel model and smooth model are encoded into the Markov random field framework to update the weights of spatio-temporal constraints.The algorithm of loopy belief propagation makes the global energy optimization more effective.Experimental results for different video sequences show our developed method has a better veracity of segmentation results.