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Machine Vision and Applications

, Volume 24, Issue 5, pp 1043–1053 | Cite as

Large-scale gaussian process multi-class classification for semantic segmentation and facade recognition

  • Björn FröhlichEmail author
  • Erik Rodner
  • Michael Kemmler
  • Joachim Denzler
Original Paper

Abstract

This paper deals with the task of semantic segmentation, which aims to provide a complete description of an image by inferring a pixelwise labeling. While pixelwise classification is a suitable approach to achieve this goal, state-of-the-art kernel methods are generally not applicable since training and testing phase involve large amounts of data. We address this problem by presenting a method for large-scale inference with Gaussian processes. Standard limitations of Gaussian process classifiers in terms of speed and memory are overcome by pre-clustering the data using decision trees. This leads to a breakdown of the entire problem into several independent classification tasks whose complexity is controlled by the maximum number of training examples allowed in the tree leaves. We additionally propose a technique which allows for computing multi-class probabilities by incorporating uncertainties of the classifier estimates. The approach provides pixelwise semantics for a wide range of applications and different image types such as those from scene understanding, defect localization, and remote sensing. Our experiments are performed with a facade recognition application that shows the significant performance gain achieved by our method compared to previous approaches.

Keywords

Large scale classification Gaussian processes Random decision forest Semantic segmentation  Facade recognition Scene interpretation 

Notes

Acknowledgments

This work was partially supported by the Graduate School on Image Processing and Image Interpretation funded by the state of Thuringia/Germany.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Björn Fröhlich
    • 1
    Email author
  • Erik Rodner
    • 1
    • 2
  • Michael Kemmler
    • 1
  • Joachim Denzler
    • 1
  1. 1.Friedrich Schiller UniversityJenaGermany
  2. 2.ICSIUC BerkeleyUSA

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