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

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.

• Type: Conference
• Year: 2009
• Where: SCALESPACE
• Authors: Jan Lellmann, Jörg H. Kappes, Jing Yuan, Florian Becker, Christoph Schnörr
• Keywords: Computer Vision, Multi-class Labeling, Multidimensional Total Variation, Operator Splitting Framework, SCALESPACE 2009