A Mutual-Information Scale-Space for Image Feature Detection and Feature-Based Classification of Volumetric Brain Images
Abstract
This paper proposes a novel information theoretic scale-space for salient feature detection, based on the mutual information (MI) of image measurement and location. The MI scale-space is designed to identify image regions whose measurements are maximally informative regarding spatial location. A framework for computing the MI scale-space is proposed, based on combining information theory with Gaussian scale-space theory, where uncertainty in spatial location is explicitly defined by the heat equation. Experiments investigate the use of MI features for feature-based classification of Alzheimer's subjects in volumetric magnetic resonance imagery from a public data set, where MI features result in higher classification accuracy than features selected according to the established difference-of-Gaussian (DOG) criterion.
Cite
Text
Toews and Iii. "A Mutual-Information Scale-Space for Image Feature Detection and Feature-Based Classification of Volumetric Brain Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543471Markdown
[Toews and Iii. "A Mutual-Information Scale-Space for Image Feature Detection and Feature-Based Classification of Volumetric Brain Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/toews2010cvprw-mutualinformation/) doi:10.1109/CVPRW.2010.5543471BibTeX
@inproceedings{toews2010cvprw-mutualinformation,
title = {{A Mutual-Information Scale-Space for Image Feature Detection and Feature-Based Classification of Volumetric Brain Images}},
author = {Toews, Matthew and Iii, William M. Wells},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2010},
pages = {111-116},
doi = {10.1109/CVPRW.2010.5543471},
url = {https://mlanthology.org/cvprw/2010/toews2010cvprw-mutualinformation/}
}