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A Bayesian Approach for Scene Interpretation with Integrated Hierarchical Structure

  • Conference paper
Pattern Recognition (DAGM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6835))

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Abstract

We propose a concept for scene interpretation with integrated hierarchical structure. This hierarchical structure is used to detect mereological relations between complex objects as buildings and their parts, e. g., windows. We start with segmenting regions at many scales, arranging them in a hierarchy, and classifying them by a common classifier. Then, we use the hierarchy graph of regions to construct a conditional Bayesian network, where the probabilities of class occurrences in the hierarchy are used to improve the classification results of the segmented regions in various scales. The interpreted regions can be used to derive a consistent scene representation, and they can be used as object detectors as well. We show that our framework is able to learn models for several objects, such that we can reliably detect instances of them in other images.

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Author information

Authors and Affiliations

  1. Institute of Applied Computer Science, Bundeswehr University Munich, Germany

    Martin Drauschke

  2. Institute of Geodesy and Geoinformation, University of Bonn, Germany

    Wolfgang Förstner

Authors
  1. Martin Drauschke
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  2. Wolfgang Förstner
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Editor information

Editors and Affiliations

  1. Visual Sensorics and Information Processing Lab, Johann-Wolfgang Goethe University, 60054, Frankfurt/Main, Germany

    Rudolf Mester

  2. Computer Vision Laboratory, Linköping University, 58183, Linköping, Sweden

    Michael Felsberg

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Drauschke, M., Förstner, W. (2011). A Bayesian Approach for Scene Interpretation with Integrated Hierarchical Structure. In: Mester, R., Felsberg, M. (eds) Pattern Recognition. DAGM 2011. Lecture Notes in Computer Science, vol 6835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23123-0_1

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  • DOI: https://doi.org/10.1007/978-3-642-23123-0_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23122-3

  • Online ISBN: 978-3-642-23123-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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