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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Lellmann, J., Kappes, J., Yuan, J., Becker, F., & Schnörr, C. (2009). Convex multi-class image labeling by simplex-constrained total variation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5567 LNCS, pp. 150–162). https://doi.org/10.1007/978-3-642-02256-2_13
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