Abstract: Various matting methods have been proposed to isolate objects from images by extracting alpha mattes. Although they typically work well for images with smooth regions, their ability to deal with complex or textured patterns is limited due to their inductive inference nature. In this paper we present a Transductive Matting algorithm which explicitly treats the matting task as a statistical transductive inference. Unlike previous approaches, we assume the user marked pixels do not fully capture the statistical distributions of foreground and background colors in the unknown region of the given trimap, thus new foreground and background colors are allowed to be recognized in the transductive labeling process. Quantitative comparisons show that our method achieves better results than previous methods on textured images.