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Semantic image labelling is the task of assigning each pixel of an image to a semantic category. To this end, in low-level image labelling, a labelled training set is available. In such a situation, structural information about the correlation between different image parts is particularly important. When a part-based inference algorithm is used to perform the association of semantic classes to pixels, however, a good choice on how to use structural information is crucial for learning an efficient and generalisable probabilistic model for the labelling task. In this paper we introduce an efficient way to take into account correlation between different image parts, embedding the parts relationships in a graph built according to aspect coherence of neighbouring image patches.