An Optimal Reduced Representation of a MoG with Applicatios to Medical Image Database Classification
Abstract
This work focuses on a general framework for image categorization, classification and retrieval that may be appropriate for medical image archives. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (MoG) along with information-theoretic image matching measures (KL). A category model is obtained by learning a reduced model from all the images in the category. We propose a novel algorithm for learning a reduced representation of a MoG, that is based on the unscented-transform. The superiority of the proposed method is validated on both simulation experiments and categorization of a real medical image database.
Cite
Text
Goldberger et al. "An Optimal Reduced Representation of a MoG with Applicatios to Medical Image Database Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383334Markdown
[Goldberger et al. "An Optimal Reduced Representation of a MoG with Applicatios to Medical Image Database Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/goldberger2007cvpr-optimal/) doi:10.1109/CVPR.2007.383334BibTeX
@inproceedings{goldberger2007cvpr-optimal,
title = {{An Optimal Reduced Representation of a MoG with Applicatios to Medical Image Database Classification}},
author = {Goldberger, Jacob and Greenspan, Hayit and Dreyfuss, Jeremie},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2007},
doi = {10.1109/CVPR.2007.383334},
url = {https://mlanthology.org/cvpr/2007/goldberger2007cvpr-optimal/}
}