A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces

Ye Z, Haefner B, Queau Y, Moellenhoff T, Cremers D (2023)

Publication Type: Journal article

Publication year: 2023

Journal

International Journal of Computer Vision Springer Verlag (Germany)

Book Volume: 131

Pages Range: 346-362

Journal Issue: 1

DOI: 10.1007/s11263-022-01704-7

Abstract

Many problems in imaging and low-level vision can be formulated as nonconvex variational problems. A promising class of approaches to tackle such problems are convex relaxation methods, which consider a lifting of the energy functional to a higher-dimensional space. However, they come with increased memory requirements due to the lifting. The present paper is an extended version of the earlier conference paper by Ye et al. (in: DAGM German conference on pattern recognition (GCPR), 2021) which combined two recent approaches to make lifting more scalable: product-space relaxation and sublabel-accurate discretization. Furthermore, it is shown that a simple cutting-plane method can be used to solve the resulting semi-infinite optimization problem. This journal version extends the previous conference work with additional experiments, a more detailed outline of the complete algorithm and a user-friendly introduction to functional lifting methods.

Involved external institutions

Technische Universität München (TUM)

Germany (DE) Université de Caen Basse-Normandie

France (FR) Riken Institute of Physical and Chemical Research / 理研

Japan (JP)

How to cite

APA:

Ye, Z., Haefner, B., Queau, Y., Moellenhoff, T., & Cremers, D. (2023). A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces. International Journal of Computer Vision, 131(1), 346-362. https://doi.org/10.1007/s11263-022-01704-7

MLA:

Ye, Zhenzhang, et al. "A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces." International Journal of Computer Vision 131.1 (2023): 346-362.

BibTeX: Download