Applying the Information Bottleneck Principle to Unsupervised Clustering of Discrete and Continuous Image Representations
Abstract
In this paper we present a method for unsupervised image clustering. The method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. The IB principle is applied to both discrete and continuous image representations, using dis-crete image histograms and probabilistic continuous image modeling based on mixture of Gaussian densities, respec-tively. Experimental results demonstrate the performance of the proposed method for image clustering on a large im-age database. Several clustering algorithms derived from the IB principle are explored and compared.
Cite
Text
Gordon et al. "Applying the Information Bottleneck Principle to Unsupervised Clustering of Discrete and Continuous Image Representations." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238368Markdown
[Gordon et al. "Applying the Information Bottleneck Principle to Unsupervised Clustering of Discrete and Continuous Image Representations." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/gordon2003iccv-applying/) doi:10.1109/ICCV.2003.1238368BibTeX
@inproceedings{gordon2003iccv-applying,
title = {{Applying the Information Bottleneck Principle to Unsupervised Clustering of Discrete and Continuous Image Representations}},
author = {Gordon, Shiri and Greenspan, Hayit and Goldberger, Jacob},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2003},
pages = {370-377},
doi = {10.1109/ICCV.2003.1238368},
url = {https://mlanthology.org/iccv/2003/gordon2003iccv-applying/}
}