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In this paper we introduce Structured Local Predictors (SLP) — A new formulation that considers the image labelling problem from a structured learning point of view. SLP are locally operating models, which provide a per-pixel labelling by exploiting contextual relations, learned from complex interactions between labels and a customizable intermediate representation of the image data. Our first key...
In this paper we propose a simple and effective way to integrate structural information in random forests for semantic image labelling. By structural information we refer to the inherently available, topological distribution of object classes in a given image. Different object class labels will not be randomly distributed over an image but usually form coherently labelled regions. In this work we...
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