关键词: 视觉统计概率模型, 支持向量机（SVM）, 自然图像抠图, 无监督抠图

Abstract: 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.

Key words: visual statistical probabilistic models, Support Vector Machine（SVM）, natural image matting, unsupervised matting