An Adaptive Clustering Algorithm for Image Segmentation

Pappas, Thrasyvoulos N.; Jayant, Nikil S.

doi:10.1109/CCV.1988.590006

An Adaptive Clustering Algorithm for Image Segmentation

Thrasyvoulos N. Pappas, Nikil S. Jayant

ICCV 1988 pp. 310-315

doi:10.1109/CCV.1988.590006 /iccv/1988/pappas1988iccv-adaptive/

Abstract

A generalization of the K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image is proposed. Spatial constraints are included by the use of a Gibbs random field model. Local intensity variations are accounted for in an iterative procedure involving averaging over a sliding window whose size decreases as the algorithm progresses. Results with an eight-neighbor Gibbs random field model applied to pictures of industrial objects and a variety of other images show that the algorithm performs better than the K-means algorithm and its nonadaptive extensions. >

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Cite

Text

Pappas and Jayant. "An Adaptive Clustering Algorithm for Image Segmentation." IEEE/CVF International Conference on Computer Vision, 1988. doi:10.1109/CCV.1988.590006

Markdown

[Pappas and Jayant. "An Adaptive Clustering Algorithm for Image Segmentation." IEEE/CVF International Conference on Computer Vision, 1988.](https://mlanthology.org/iccv/1988/pappas1988iccv-adaptive/) doi:10.1109/CCV.1988.590006

BibTeX

@inproceedings{pappas1988iccv-adaptive,
  title     = {{An Adaptive Clustering Algorithm for Image Segmentation}},
  author    = {Pappas, Thrasyvoulos N. and Jayant, Nikil S.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1988},
  pages     = {310-315},
  doi       = {10.1109/CCV.1988.590006},
  url       = {https://mlanthology.org/iccv/1988/pappas1988iccv-adaptive/}
}