Adaptive Video Segmentation Algorithm Using Hidden Conditional Random Fields
基于隐条件随机场的自适应视频分割算法

Video object segmentation is important for video surveillance and video object tracking,video object recog- nition and video editing.An adaptive video segmentation algorithm based on hidden conditional random fields(HCRFs) is proposed,which models spatio-temporal constraints of video sequence.In order to improve the segmentation quality, the weights of spatio-temporal constraints are adaptively updated by on-line learning of HCRFs.The experimental results have demonstrated that the error ratio of our algorithm is reduced by 23% and 19%,respectively,compared with Gaussian mixture model(GMM)and spatio-temporal Markov random fields(MRF).