Transductive segmentation

We consider a multi-zone segmentation of a single image when user-supplied seeds are provided in each region. We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to be classified. Our method relies on the Laplacian graph regularizer, a powerful manifold-learning tool that is based on the estimation of variants of the Laplace-Beltrami operator and that is tightly related to diffusion processes. Our segmentation is modeled as the task of finding matting coefficients for unclassified pixels given known matting coefficients of seed pixels. The resulting segmentation procedure is simple, fast, and accurate. Comparison with other methods on natural images databases are given.