TY  - GEN
TI  - Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation
Y1  - 2008///
T3  - IWR-Preprints
AV  - public
ID  - heidok8759
A1  - Lellmann, Jan
A1  - Kappes, Jörg
A1  - Yuan, Jing
A1  - Becker, Florian
A1  - Schnörr, Christoph
N2  - 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.
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/8759/
ER  -