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As Time Goes by—Anytime Semantic Segmentation with Iterative Context Forests

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 7476)

Abstract

We present a new approach for contextual semantic segmentation and introduce a new tree-based framework, which combines local information and context knowledge in a single model. The method itself is also suitable for anytime classification scenarios, where the challenge is to estimate a label for each pixel in an image while allowing an interruption of the estimation at any time. This offers the application of the introduced method in time-critical tasks, like automotive applications, with limited computational resources unknown in advance. Label estimation is done in an iterative manner and includes spatial context right from the beginning. Our approach is evaluated in extensive experiments showing its state-of-the-art performance on challenging street scene datasets with anytime classification abilities.

Keywords

  • Random Forest
  • Context Feature
  • Average Recognition Rate
  • Street Scene
  • Semantic Segmentation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fröhlich, B., Rodner, E., Denzler, J. (2012). As Time Goes by—Anytime Semantic Segmentation with Iterative Context Forests. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-32717-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32716-2

  • Online ISBN: 978-3-642-32717-9

  • eBook Packages: Computer ScienceComputer Science (R0)