  In this paper, we cast the scribble-based interactive image segmentation as a
semi-supervised learning problem. Our novel approach alleviates the need to
solve an expensive generalized eigenvector problem by approximating the
eigenvectors using efficiently computed eigenfunctions. The smoothness operator
defined on feature densities at the limit n tends to infinity recovers the
exact eigenvectors of the graph Laplacian, where n is the number of nodes in
the graph. To further reduce the computational complexity without scarifying
our accuracy, we select pivots pixels from user annotations. In our
experiments, we evaluate our approach using both human scribble and "robot
user" annotations to guide the foreground/background segmentation. We developed
a new unbiased collection of five annotated images datasets to standardize the
evaluation procedure for any scribble-based segmentation method. We
experimented with several variations, including different feature vectors,
pivot count and the number of eigenvectors. Experiments are carried out on
datasets that contain a wide variety of natural images. We achieve better
qualitative and quantitative results compared to state-of-the-art interactive
segmentation algorithms.
