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Asymmetric Cuts: Joint Image Labeling and Partitioning

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Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

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Abstract

For image segmentation, recent advances in optimization make it possible to combine noisy region appearance terms with pairwise terms which can not only discourage, but also encourage label transitions, depending on boundary evidence. These models have the potential to overcome problems such as the shrinking bias. However, with the ability to encourage label transitions comes a different problem: strong boundary evidence can overrule weak region appearance terms to create new regions out of nowhere. While some label classes exhibit strong internal boundaries, such as the background class which is the pool of objects. Other label classes, meanwhile, should be modeled as a single region, even if some internal boundaries are visible.

We therefore propose in this work to treat label classes asymmetrically: for some classes, we allow a further partitioning into their constituent objects as supported by boundary evidence; for other classes, further partitioning is forbidden. In our experiments, we show where such a model can be useful for both 2D and 3D segmentation.

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Authors and Affiliations

  1. Multidimensional Image Processing Group, Heidelberg University, Heidelberg, Germany

    Thorben Kroeger, Thorsten Beier, Ullrich Koethe & Fred A. Hamprecht

  2. Heidelberg Collaboratory for Image Processing, Heidelberg University, Heidelberg, Germany

    Jörg H. Kappes & Fred A. Hamprecht

Authors
  1. Thorben Kroeger
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  2. Jörg H. Kappes
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  3. Thorsten Beier
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  4. Ullrich Koethe
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  5. Fred A. Hamprecht
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Correspondence to Thorben Kroeger .

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Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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Kroeger, T., Kappes, J.H., Beier, T., Koethe, U., Hamprecht, F.A. (2014). Asymmetric Cuts: Joint Image Labeling and Partitioning. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_16

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  • Publisher Name: Springer, Cham

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