一种在线学习的视频图像分割算法

提出了一种在线学习的视频图像分割算法,通过结合视频图像的全局信息和局部信息,来完成视频图像的准确分割。该算法首先采用分类器对视频图像的无指导预分割结果进行整体的识别处理,得到粗糙的像素级前后景分割图像,再通过时空条件随机场最优化完成局部平滑处理,得到最终精确的像素级前后景分割图像。同时还提出了一种平衡采样策略和一种基于分割图像指导的样本更新算法,分别用以实现分类器准确的初始化和高效稳定的在线学习。基于真实视频序列的实验表明,相比已有方法,算法在低时间开销下,显著提高了分割的准确性与稳定性。

Online Learning Based Video Segmentation Algorithm

A novel online learning based video segmentation algorithm was proposed,combining both the global and local information of video images.The videos were pre-segmented by the unsupervised image segmentation method firstly,and then the coarse foreground was extracted by the detection of the classifier.After that,the final optimal pixel-wise segmentation was achieved by using spatial-temporal Conditional Random Fields,and the classifier was updated with the constraints of the segmentation result.Meanwhile,a balance sampling strategy and a sample-updating approach supervised by segmentation were proposed,to improve the accuracy and stability of the classifier on initialization and updating separately.Experiments on challenging video sequences show that the proposed method highly improves the precision and the stability of video segmentation with low time cost,compared to state-of-the-art methods.