Introducing Geometry in Active Learning for Image Segmentation

Ksenia Konyushkova, Raphael Sznitman, Pascal Fua

ICCV 2015

doi:10.1109/ICCV.2015.340 /iccv/2015/konyushkova2015iccv-introducing/

Abstract

We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes. To this end, we use these priors not only to select voxels most in need of annotation but to guarantee that they lie on 2D planar patch, which makes it much easier to annotate than if they were randomly distributed in the volume. A simplified version of this approach is effective in natural 2D images. We evaluated our approach on Electron Microscopy and Magnetic Resonance image volumes, as well as on natural images. Comparing our approach against several accepted baselines demonstrates a marked performance increase.

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Cite

Text

Konyushkova et al. "Introducing Geometry in Active Learning for Image Segmentation." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.340

Markdown

[Konyushkova et al. "Introducing Geometry in Active Learning for Image Segmentation." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/konyushkova2015iccv-introducing/) doi:10.1109/ICCV.2015.340

BibTeX

@inproceedings{konyushkova2015iccv-introducing,
  title     = {{Introducing Geometry in Active Learning for Image Segmentation}},
  author    = {Konyushkova, Ksenia and Sznitman, Raphael and Fua, Pascal},
  booktitle = {International Conference on Computer Vision},
  year      = {2015},
  doi       = {10.1109/ICCV.2015.340},
  url       = {https://mlanthology.org/iccv/2015/konyushkova2015iccv-introducing/}
}