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Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

  • Alexander KolesnikovEmail author
  • Christoph H. Lampert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9908)

Abstract

We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.

Keywords

Weakly-supervised image segmentation Deep learning 

Notes

Acknowledgments

This work was funded by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 308036. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. We also thank Vittorio Ferrari for helpful feedback.

Supplementary material

419976_1_En_42_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (pdf 1168 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.IST AustriaKlosterneuburgAustria

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