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In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images\udof man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range\udconnections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge\udpotentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field\udalgorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous\udstate-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD