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Efficient Facade Segmentation using Auto-Context


Conference Paper


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.

Author(s): Varun Jampani and Raghudeep Gadde and Peter V. Gehler
Book Title: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on
Pages: 1038--1045
Year: 2015
Month: January
Publisher: IEEE

Department(s): Perzeptive Systeme
Research Project(s): Facade Segmentation
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1109/WACV.2015.143
Event Name: WACV, 2015
Event Place: Waikoloa, HI, US

Organization: IEEE
URL: http://wacv2015.org

Links: website
IEEE page


  title = {Efficient Facade Segmentation using Auto-Context},
  author = {Jampani, Varun and Gadde, Raghudeep and Gehler, Peter V.},
  booktitle = {Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on},
  pages = {1038--1045},
  publisher = {IEEE},
  organization = {IEEE},
  month = jan,
  year = {2015},
  url = {http://wacv2015.org},
  month_numeric = {1}