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Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation

Lellmann, Jan and Kappes, Jörg and Yuan, Jing and Becker, Florian and Schnörr, Christoph

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Abstract

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.

Item Type: Preprint
Series Name: IWR-Preprints
Date Deposited: 06. Nov 2008 14:04
Date: 2008
Faculties / Institutes: Service facilities > Interdisciplinary Center for Scientific Computing
Subjects: 510 Mathematics
Controlled Keywords: Bildverarbeitung, Konvexe Optimierung, Diskrete Optimierung, Funktion von beschränkter Variation
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