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Segmentation and Classification of Objects with Implicit Scene Context

  • Conference paper
Outdoor and Large-Scale Real-World Scene Analysis

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7474))

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Abstract

We present a novel approach to segment and classify objects in images into two classes. A binary conditional random field (CRF) framework is augmented with an unsupervised clustering step learning contextual relations of objects, the so-called implicit scene context (ISC). Several experiments with simulated data, images from benchmark data sets, and aerial images of an urban area show improved results compared to a standard CRF.

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Author information

Authors and Affiliations

  1. Institute of Photogrammetry and GeoInformation, Germany

    Jan D. Wegner & Uwe Sörgel

  2. Institut für Informationsverarbeitung, Leibniz Universität Hannover, Germany

    Bodo Rosenhahn

Authors
  1. Jan D. Wegner
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  2. Bodo Rosenhahn
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  3. Uwe Sörgel
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Editor information

Editors and Affiliations

  1. College of Computing Building, Georgia Institute of Technology, 801 Atlantic Drive, 30332-0280, Atlanta, GA, USA

    Frank Dellaert

  2. Department of Computer Science, University of North Carolina at Chapel Hill, Brooks Computer Science Building, CB#3175, 27599, Chapel Hill, NC, USA

    Jan-Michael Frahm

  3. CVG - Institute of Visual Computing, Department of Computer Science, ETH Zurich, CNB G105, Universitaetstrasse 6, 8092, Zurich, Switzerland

    Marc Pollefeys

  4. Institute for Information Processing (TNT), Leibniz Universität, Appelstr. 9A, 30167, Hannover, Germany

    Laura Leal-Taixé

  5. Institute for Information Processing (TNT), Leibniz Universität, Appelstr. 9A, 30167, Hannover, Germany

    Bodo Rosenhahn

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Wegner, J.D., Rosenhahn, B., Sörgel, U. (2012). Segmentation and Classification of Objects with Implicit Scene Context. In: Dellaert, F., Frahm, JM., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds) Outdoor and Large-Scale Real-World Scene Analysis. Lecture Notes in Computer Science, vol 7474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34091-8_12

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  • DOI: https://doi.org/10.1007/978-3-642-34091-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34090-1

  • Online ISBN: 978-3-642-34091-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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