Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Procedural modeling of buildings
ACM SIGGRAPH 2006 Papers
Image-based procedural modeling of facades
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH Asia 2008 papers
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Image-based street-side city modeling
ACM SIGGRAPH Asia 2009 papers
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Unsupervised facade segmentation using repetitive patterns
Proceedings of the 32nd DAGM conference on Pattern recognition
Adaptive partitioning of urban facades
Proceedings of the 2011 SIGGRAPH Asia Conference
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Shape grammar parsing via Reinforcement Learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Interactive Coherence-Based Façade Modeling
Computer Graphics Forum
2D-3D fusion for layer decomposition of urban facades
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper presents an approach to address the problem of image façade labelling. In the architectural literature, domain knowledge is usually expressed geometrically in the final design, so façade labelling should on the one hand conform to visual evidence, and on the other hand to the architectural principles – how individual assets (e.g. doors, windows) interact with each other to form a façade as a whole. To this end, we first propose a recursive splitting method to segment façades into a bunch of tiles for semantic recognition. The segmentation improves the processing speed, guides visual recognition on suitable scales and renders the extraction of architectural principles easy. Given a set of segmented training façades with their label maps, we then identify a set of meta-features to capture both the visual evidence and the architectural principles. The features are used to train our façade labelling model. In the test stage, the features are extracted from segmented façades and the inferred label maps. The following three steps are iterated until the optimal labelling is reached: 1) proposing modifications to the current labelling; 2) extracting new features for the proposed labelling; 3) feeding the new features to the labelling model to decide whether to accept the modifications. In experiments, we evaluated our method on the ECP façade dataset and achieved higher precision than the state-of-the-art at both the pixel level and the structural level.