A Deep-Structured Fully Connected Random Field Model for Structured Inference

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 paper, 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 autoencoding layers placed between state layers to significantly reduce the 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 paper 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.