Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs
Abstract
This paper introduces a novel algorithm for transductive inference in higher-order MRFs, where the unary energies are parameterized by a variable classifier. The considered task is posed as a joint optimization problem in the continuous classifier parameters and the discrete label variables. In contrast to prior approaches such as convex relaxations, we propose an advantageous decoupling of the objective function into discrete and continuous subproblems and a novel, efficient optimization method related to ADMM. This approach preserves integrality of the discrete label variables and guarantees global convergence to a critical point. We demonstrate the advantages of our approach in several experiments including video object segmentation on the DAVIS data set and interactive image segmentation.
Cite
@article{arxiv.1705.05020,
title = {Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs},
author = {Emanuel Laude and Jan-Hendrik Lange and Jonas Schüpfer and Csaba Domokos and Laura Leal-Taixé and Frank R. Schmidt and Bjoern Andres and Daniel Cremers},
journal= {arXiv preprint arXiv:1705.05020},
year = {2018}
}