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Multi-instance Methods for Partially Supervised Image Segmentation

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Partially Supervised Learning (PSL 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7081))

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Abstract

In this paper, we propose a new partially supervised multi-class image segmentation algorithm. We focus on the multi-class, single-label setup, where each image is assigned one of multiple classes. We formulate the problem of image segmentation as a multi-instance task on a given set of overlapping candidate segments. Using these candidate segments, we solve the multi-instance, multi-class problem using multi-instance kernels with an SVM. This computationally advantageous approach, which requires only convex optimization, yields encouraging results on the challenging problem of partially supervised image segmentation.

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Authors and Affiliations

  1. Autonomous Intelligent Systems Department of Computer Science, University of Bonn, 53113, Bonn, Germany

    Andreas Müller & Sven Behnke

Authors
  1. Andreas Müller
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  2. Sven Behnke
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Editor information

Friedhelm Schwenker Edmondo Trentin

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Müller, A., Behnke, S. (2012). Multi-instance Methods for Partially Supervised Image Segmentation. In: Schwenker, F., Trentin, E. (eds) Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science(), vol 7081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28258-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-28258-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28257-7

  • Online ISBN: 978-3-642-28258-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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