  This paper proposes a novel MAP inference framework for Markov Random Field
(MRF) in parallel computing environments. The inference framework, dubbed Swarm
Fusion, is a natural generalization of the Fusion Move method. Every thread (in
a case of multi-threading environments) maintains and updates a solution. At
each iteration, a thread can generate arbitrary number of solution proposals
and take arbitrary number of concurrent solutions from the other threads to
perform multi-way fusion in updating its solution. The framework is general,
making popular existing inference techniques such as alpha-expansion, fusion
move, parallel alpha-expansion, and hierarchical fusion, its special cases. We
have evaluated the effectiveness of our approach against competing methods on
three problems of varying difficulties, in particular, the stereo, the optical
flow, and the layered depthmap estimation problems.
