Interactive image segmentation via minimization of quadratic energies on directed graphs

We propose a scheme to introduce directionality in the Random Walker algorithm for image segmentation. In particular, we extend the optimization framework of this algorithm to combinatorial graphs with directed edges. Our scheme is interactive and requires the user to label a few pixels that are representative of a foreground object and of the background. These labeled pixels are used to learn intensity models for the object and the background, which allow us to automatically set the weights of the directed edges. These weights are chosen so that they bias the direction of the object boundary gradients to flow from regions that agree well with the learned object intensity model to regions that do not agree well. We use these weights to define an energy function that associates asymmetric quadratic penalties with the edges in the graph. We show that this energy function is convex, hence it has a unique minimizer. We propose a provably convergent iterative algorithm for minimizing this ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Dheeraj Singaraju, Leo Grady, René Vidal
• Keywords: Computer Vision, Convergent Iterative Algorithm, CVPR 2008, Energy Function, Object Boundary Gradients, Object Intensity Model, Random Walker Algorithm