ML Anthology

Applying Incremental Learning to Parallel Image Segmentation

Cyril Charron, Yulia Hicks, Peter Hall
ICCVW 2009 pp. 2064-2071
doi:10.1109/ICCVW.2009.5457535 /iccvw/2009/charron2009iccvw-applying/

Abstract

Segmenting large or multiple images is time and memory consuming. These issues have been addressed in the past by implementing parallel versions of popular algorithms such as Graph Cuts and Mean Shift. Here, we propose to use an incremental Gaussian Mixture Model (GMM) learning algorithm for parallel image segmentation. We show that our approach allows us to reduce the memory requirements dramatically whilst obtaining high quality of segmentation. We also compare memory, time and quality of the performance of our approach and several other state of the art segmentation algorithms in a rigorous set of experiments, which produce favorable results.

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Cite

Text

Charron et al. "Applying Incremental Learning to Parallel Image Segmentation." IEEE/CVF International Conference on Computer Vision Workshops, 2009. doi:10.1109/ICCVW.2009.5457535

Markdown

[Charron et al. "Applying Incremental Learning to Parallel Image Segmentation." IEEE/CVF International Conference on Computer Vision Workshops, 2009.](https://mlanthology.org/iccvw/2009/charron2009iccvw-applying/) doi:10.1109/ICCVW.2009.5457535

BibTeX

@inproceedings{charron2009iccvw-applying,
  title     = {{Applying Incremental Learning to Parallel Image Segmentation}},
  author    = {Charron, Cyril and Hicks, Yulia and Hall, Peter},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2009},
  pages     = {2064-2071},
  doi       = {10.1109/ICCVW.2009.5457535},
  url       = {https://mlanthology.org/iccvw/2009/charron2009iccvw-applying/}
}