%0 Generic
%A Lellmann, Jan
%A Kappes, Jörg
%A Yuan, Jing
%A Becker, Florian
%A Schnörr, Christoph
%D 2008
%F heidok:8759
%R 10.11588/heidok.00008759
%T Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation
%U https://archiv.ub.uni-heidelberg.de/volltextserver/8759/
%X Multi-class labeling is one of the core problems in image analysis. We show how this combinatorial problem can be approximately solved using tools from convex optimization. We suggest a novel functional based on a multidimensional total variation formulation, allowing for a broad range of data terms. Optimization is carried out in the operator splitting framework using Douglas-Rachford Splitting. In this connection, we compare two methods to solve the Rudin-Osher-Fatemi type subproblems and demonstrate the performance of our approach on single- and multichannel images.