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In image semantic segmentation a semantic category label is associated to each image pixel. This classification problem is characterised by pixel dependencies at different scales. On a small-scale pixel correlation is related to object instance sharing, whereas on a middle- and large scale to category co-presence and relative location constraints. The contribution of this study is two-fold. First,...
This paper addresses the problem of image semantic segmentation (or semantic labelling), that is the association of one of a predefined set of semantic categories (e.g. cow, car, face) to each image pixel. We adopt a patch-based approach, in which super-pixel elements are obtained via oversegmentation of the original image. We then train a conditional random field on heterogeneous descriptors extracted...
Part-based approaches in image analysis aim at exploiting the considerable discriminative power embedded in relations among image parts. Nonetheless, learning structural information is not always possible without the availability of a training set of classified parts, and taking into account this additional information can even degrade the performance of the system. In this paper, a discriminative...
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