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A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces.
- Source :
-
International Journal of Computer Vision . Jan2023, Vol. 131 Issue 1, p346-362. 17p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09205691
- Volume :
- 131
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- International Journal of Computer Vision
- Publication Type :
- Academic Journal
- Accession number :
- 161158853
- Full Text :
- https://doi.org/10.1007/s11263-022-01704-7