%0 Journal Article %A MA Chen %A LI Gang %A ZHANG Renbin %A ZHANG Huijun %A QIN Yajun %A XIE Zhao %T Visual statistical probability based unsupervised digital matting models %D 2015 %R %J Computer Engineering and Applications %P 169-174 %V 51 %N 18 %X According to the problem of the large visual deviation of matting results due to the less visual information, an unsupervised-matting model based on the visual statistical probability is proposed. The method trains SVM classifier based on the above model to get the SIFT feature point which distinguishes the background area and the higher visual degree foreground target area. And then it generates well-structured Trimap according to the feature points. The unsupervised-matting is achieved by the use of Trimap. Experimental results show that in the case of without user interaction, the model generates [α] mask without large visual deviation, makes good estimate of the foreground object edges and transparency and has better robustness. %U http://cea.ceaj.org/EN/abstract/article_33664.shtml