ML Anthology

Natural Image Statistics for Natural Image Segmentation

Matthias Heiler, Christoph Schnörr
ICCV 2003 pp. 1259-1266
doi:10.1109/ICCV.2003.1238635 /iccv/2003/heiler2003iccv-natural/

Abstract

Building on recent progress in modeling filter response statistics of natural images we integrate a statistical model into a variational framework for image segmentation. Incorporated in a sound probabilistic distance measure the model drives level sets toward meaningful segmentations of complex textures and natural scenes. Since each region comprises two model parameters only the approach is computationally efficient and enables the application of variational segmentation to a considerably larger class of real-world images. We validate the statistical basis of our approach on thousands of natural images and demonstrate that our model outperforms recent variational segmentation methods based on second-order statistics.

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Cite

Text

Heiler and Schnörr. "Natural Image Statistics for Natural Image Segmentation." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238635

Markdown

[Heiler and Schnörr. "Natural Image Statistics for Natural Image Segmentation." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/heiler2003iccv-natural/) doi:10.1109/ICCV.2003.1238635

BibTeX

@inproceedings{heiler2003iccv-natural,
  title     = {{Natural Image Statistics for Natural Image Segmentation}},
  author    = {Heiler, Matthias and Schnörr, Christoph},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2003},
  pages     = {1259-1266},
  doi       = {10.1109/ICCV.2003.1238635},
  url       = {https://mlanthology.org/iccv/2003/heiler2003iccv-natural/}
}