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Article Dans Une Revue International Journal of Computer Vision Année : 2023

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

Résumé

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. (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.
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Dates et versions

hal-03838621 , version 1 (03-11-2022)

Identifiants

Citer

Zhenzhang Ye, Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers. A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces. International Journal of Computer Vision, 2023, 131 (1), pp.346-362. ⟨10.1007/s11263-022-01704-7⟩. ⟨hal-03838621⟩
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