A multiscale algorithm for image segmentation by variational method
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We study an advanced method for supervised multi-label image segmentation. To this end, we adopt a classic framework which recently has been revitalised by Rhemann et al. (2011). Instead of the usual global energy minimisation step, it relies on a mere evaluation of a cost function for every solution label, which is followed by a spatial smoothing step of these costs. While Rhemann et al. concentrate on efficiency, the goal of this paper is to equip the general framework with sophisticated subcomponents in order to develop a high-quality method for multi-label image segmentation: First, we present a substantially improved cost computation scheme which incorporates texture descriptors, as well as an automatic feature selection strategy. This leads to a high-dimensional feature space, from which we extract the label costs using a support vector machine. Second, we present a novel anisotropic diffusion scheme for the filtering step. In this PDE-based process, the smoothing of the cost volume is steered along the structures of the previously computed feature space. Experiments on widely used image databases show that our scheme produces segmentations of clearly superior quality.