  Graph cuts-based algorithms have achieved great success in energy
minimization for many computer vision applications. These algorithms provide
approximated solutions for multi-label energy functions via move-making
approach. This approach fuses the current solution with a proposal to generate
a lower-energy solution. Thus, generating the appropriate proposals is
necessary for the success of the move-making approach. However, not much
research efforts has been done on the generation of "good" proposals,
especially for non-metric energy functions. In this paper, we propose an
application-independent and energy-based approach to generate "good" proposals.
With these proposals, we present a graph cuts-based move-making algorithm
called GA-fusion (fusion with graph approximation-based proposals). Extensive
experiments support that our proposal generation is effective across different
classes of energy functions. The proposed algorithm outperforms others both on
real and synthetic problems.
