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This paper proposes the sequential context inference (SCI) algorithm for Markov random field (MRF) image analysis. This algorithm is designed primarily for fast inference on an MRF model, but its application requires also a specific modeling architecture. The architecture is composed of a sequence of stages, each modeling the conditional probability of the labels, conditioned on a neighborhood of the input image and output of the previous stage. By learning the model at each stage sequentially with regards to the true output labels, the stages learn different models which can cope with errors in the previous stage.