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Random fields have remained a topic of great interest over past decades forthe purpose of structured inference, especially for problems such as imagesegmentation. The local nodal interactions commonly used in such models oftensuffer the short-boundary bias problem, which are tackled primarily through theincorporation of long-range nodal interactions. However, the issue ofcomputational tractability becomes a significant issue when incorporating suchlong-range nodal interactions, particularly when a large number of long-rangenodal interactions (e.g., fully-connected random fields) are modeled.  In this work, we introduce a generalized random field framework based aroundthe concept of stochastic cliques, which addresses the issue of computationaltractability when using fully-connected random fields by stochastically forminga sparse representation of the random field. The proposed framework allows forefficient structured inference using fully-connected random fields without anyrestrictions on the potential functions that can be utilized. Severalrealizations of the proposed framework using graph cuts are presented andevaluated, and experimental results demonstrate that the proposed framework canprovide competitive performance for the purpose of image segmentation whencompared to existing fully-connected and principled deep random fieldframeworks.

Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields