Pyramidal Model for Image Semantic Segmentation

Giuseppe Passino, Ioannis Patras, Ebroul Izquierdo

Abstract: We present a new hierarchical model applied to the problem of image semantic segmentation, that is, the association to each pixel in an image with a category label (e.g. tree, cow, building, ...). This problem is usually addressed with a combination of an appearance-based pixel classification and a pixel context model. In our proposal, the images are initially over-segmented in dense patches. The proposed pyramidal model naturally embeds the compositional nature of a scene to achieve a multi-scale contextualisation of patches. This is obtained by imposing an order on the patches aggregation operations towards the final scene. The nodes of the pyramid (that is, a dendrogram) thus represent patch clusters, or super-patches. The probabilistic model favours the homogeneous labelling of super-patches that are likely to contain a single object instance, modelling the uncertainty in identifying such super-patches. The proposed model has several advantages, including the computational efficiency, as well as the expandability. Initial results place the model in line with other works in the recent literature.