如果背景中光线变化,那么视频图像分割将会变得比较困难。为了对光线变化的图像进行顺利侵害,提出了一种利用贝叶斯学习方法来进行视频图像分割的算法,即先在每个像素点处对不断变化的背景建模,同时计算每个像素点处的颜色直方图,再用这些直方图来表示该像素点处特征向量的概率分布,然后用贝叶斯学习方法来进行判断,以确定在光线缓慢或者突然变化的时候,每个像素点是属于前景还是属于背景。

Segmentation becomes a difficult task when the background illumination changes.In this paper,we apply a Bayesian learning method into video segmentation.The constantly changing background has been modeled at the pixel level.The feature vector for each pixel is represented with a discrete probability distribution function.The histogram colors and co-occurrence vectors have been calculated.Bayesian learning has been used to obtain these probability distribution functions from the video image inputs.The experimental results indicate that the proposed approach is able to learn a complex background of which the illumination changes either gradually or suddenly.