We introduce a new technique that can reduce any
higher-order Markov random field with binary labels into
a first-order one that has the same minima as the original.
Moreover, we combine the reduction with the fusion-move
and QPBO algorithms to optimize higher-order multi-label
problems. While many vision problems today are formulated
as energy minimization problems, they have mostly
been limited to using first-order energies, which consist of
unary and pairwise clique potentials, with a few exceptions
that consider triples. This is because of the lack of efficient
algorithms to optimize energies with higher-order interactions.
Our algorithm challenges this restriction that limits
the representational power of the models, so that higherorder
energies can be used to capture the rich statistics of
natural scenes. To demonstrate the algorithm, we minimize
a third-order energy, which allows clique potentials with up
to four pixels, in an image restoration problem. The probl...