Curvature Regularity for Multi-label Problems - Standard and Customized Linear Programming

Abstract. We follow recent work by Schoenemann et al. [25] for expressing curvature regularity as a linear program. While the original formulation focused on binary segmentation, we address several multi-label problems, including segmentation, denoising and inpainting, all cast as a single linear program. Our multi-label segmentation introduces a “curvature Potts model” and combines a well-known Potts model relaxation [14] with the above work. For inpainting, we improve on [25] by grouping intensities into bins. Finally, we address the problem of denoising with absolute differences in the data term. Furthermore, we explore alternative solving strategies, including higher order Markov Random Fields, min-sum diffusion and a combination of augmented Lagrangians and an accelerated first order scheme to solve the linear programs.

• Type: Journal
• Year: 2011
• Where: EMMCVPR
• Authors: Thomas Schoenemann, Yubin Kuang, Fredrik Kahl
• Keywords: Computer Vision, Denoising, EMMCVPR 2011, Linear Program, Potts Model