Efficient label propagation for interactive image segmentation

Abstract

A novel algorithm for interactive multilabel image/video segmentation is proposed in this paper. Given a small number of pixels with user-defined (or pre-defined) labels, our method can automatically propagate those labels to the remaining unlabeled pixels through an iterative procedure. Theoretical analysis of the convergence property of this algorithm is developed along with the corresponding connections with energy minimization of the hidden Markov random field models. To make the algorithm more efficient, we also derive a multi-level way for propagating the labels. Finally the segmentation results on natural images are presented to show the effectiveness of our method.