  There has been significant interest in the use of fully-connected graphical
models and deep-structured graphical models for the purpose of structured
inference. However, fully-connected and deep-structured graphical models have
been largely explored independently, leaving the unification of these two
concepts ripe for exploration. A fundamental challenge with unifying these two
types of models is in dealing with computational complexity. In this study, we
investigate the feasibility of unifying fully-connected and deep-structured
models in a computationally tractable manner for the purpose of structured
inference. To accomplish this, we introduce a deep-structured fully-connected
random field (DFRF) model that integrates a series of intermediate sparse
auto-encoding layers placed between state layers to significantly reduce
computational complexity. The problem of image segmentation was used to
illustrate the feasibility of using the DFRF for structured inference in a
computationally tractable manner. Results in this study show that it is
feasible to unify fully-connected and deep-structured models in a
computationally tractable manner for solving structured inference problems such
as image segmentation.
