Seeded Laplacian: An interactive image segmentation approach using eigenfunctions
Ahmed Taha, Marwan Torki · 2015
In this paper, we cast the scribbled-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 a more efficiently computed eigenfunctions. The smoothness operator defined on feature densities at the limit n → ∞ recovers the exact eigenvectors of the graph Laplacian, where n is the number of nodes in the graph. In our experiments scribble annotation is applied, where users label few pixels as foreground and background to guide the foreground/background segmentation. Experiments are carried out on standard data-sets which contain a wide variety of natural images. We achieve better qualitative and quantitative results compared to state-of-the-art algorithms.