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