We propose an algorithm that provides a pixel-wise classification of building facades. Building facades provide a rich environment for testing semantic segmentation techniques. They come in a variety of styles that reflect both appearance and layout characteristics. On the other hand, they exhibit a degree of stability in the arrangement of structures across different instances. We integrate appearance and layout cues in a single framework. The most likely label based on appearance is obtained through applying the state-of-the-art deep convolution networks. This is further optimized through Restricted Boltzmann Machines (RBM), applied on vertical and horizontal scanlines of facade models. Learning the probability distributions of the models via the RBMs is utilized in two settings. Firstly, we use them in learning from pre-seen facade samples, in the traditional training sense. Secondly, we learn from the test image at hand, in a way the allows the transfer of visual knowledge of the scene from correctly classified areas to others. Experimentally, we are on par with the reported performance results. However, we do not explicitly specify any hand-engineered features that are architectural scene dependent, nor do we include any dataset specific heuristics/thresholds.

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We propose an algorithm that provides a pixel-wise classification of building facades. Building facades provide a rich environment for testing semantic segmentation techniques. They come in a variety of styles affecting appearance and layout. On the other hand, they exhibit a degree of stability in the arrangement of structures across different instances. Furthermore, a single image is often composed of a repetitive architectural pattern. We integrate appearance, layout and repetition cues in a single energy function, that is optimized through the TRW-S algorithm to provide a classification of superpixels. The appearance energy is based on scores of a Random Forrest classifier. The feature space is composed of higher-level vectors encoding distance to structure clusters. Layout priors are obtained from locations and structural adjacencies in training data. In addition, priors result from translational symmetry cues acquired from the scene itself through clustering via the α-expansion graphcut algorithm. We are on par with state-of-the-art. We are able to fine tune classifications at the superpixel level, while most methods model all architectural features with bounding rectangles.