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Efficient Facade Segmentation Using Auto-context


In this paper we propose a system for the problem of facade segmentation. Building facades are highly structured images and consequently most methods that have been proposed for this problem, aim to make use of this strong prior information. We are describing a system that is almost domain independent and consists of standard segmentation methods. A sequence of boosted decision trees is stacked using auto-context features and learned using the stacked generalization technique. We find that this, albeit standard, technique performs better, or equals, all previous published empirical results on all available facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test time inference.
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Dates et versions

hal-01801045 , version 1 (28-05-2018)



Varun Jampani, Raghudeep Gadde, Peter Gehler. Efficient Facade Segmentation Using Auto-context. 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), Jan 2015, Waikoloa, United States. ⟨10.1109/WACV.2015.143⟩. ⟨hal-01801045⟩
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