Agglomerative Clustering on Range Data with a Unified Probabilistic Merging Function and Termination Criterion
Abstract
Clustering methods, which are frequently employed for region-based segmentation, are inherently metric based. A fundamental problem with an estimation-based criterion is that as the amount of information in a region decreases, the parameter estimates become extremely unreliable and incorrect decisions are likely to be made. It is shown that clustering need not be metric based. A rigorous region merging probability function is used. It makes use of all information available in the probability densities of a statistical image model. By using this probability function as a termination criterion it is possible to produce segmentations in which all region merges are performed above some level of confidence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
LaValle et al. "Agglomerative Clustering on Range Data with a Unified Probabilistic Merging Function and Termination Criterion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.341182Markdown
[LaValle et al. "Agglomerative Clustering on Range Data with a Unified Probabilistic Merging Function and Termination Criterion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/lavalle1993cvpr-agglomerative/) doi:10.1109/CVPR.1993.341182BibTeX
@inproceedings{lavalle1993cvpr-agglomerative,
title = {{Agglomerative Clustering on Range Data with a Unified Probabilistic Merging Function and Termination Criterion}},
author = {LaValle, Steven M. and Moroney, Kenneth J. and Hutchinson, Seth},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1993},
pages = {798-799},
doi = {10.1109/CVPR.1993.341182},
url = {https://mlanthology.org/cvpr/1993/lavalle1993cvpr-agglomerative/}
}