@inproceedings{40cfde4d46634b3c9036c622b63c4c6c,
title = "Unsupervised image clustering using the information bottleneck method",
abstract = "A new method for unsupervised image category clustering is presented, based on a continuous version of a recently introduced information theoretic principle, the information bottleneck (IB). The clustering method is based on hierarchical grouping: Utilizing a Gaussian mixture model, each image in a given archive is first represented as a set of coherent regions in a selected feature space. Images are next grouped such that the mutual information between the clusters and the image content is maximally preserved. The appropriate number of clusters can be determined directly from the IB principle. Experimental results demonstrate the performance of the proposed clustering method on a real image database.",
keywords = "Gaussian mixture modeling, Image categories, Image grouping, Information bottleneck, Kullback-leibler distance, Unsupervised clustering",
author = "Jacob Goldberger and Hayit Greenspan and Shiri Gordon",
year = "2002",
doi = "10.1007/3-540-45783-6_20",
language = "אנגלית",
isbn = "354044209X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "158--165",
editor = "{Van Gool}, Luc and {Van Gool}, Luc and {Van Gool}, Luc",
booktitle = "Pattern Recognition - 24th DAGM Symposium, Proceedings",
address = "גרמניה",
note = "24th Symposium of the German Pattern Recognition Association, DAGM 2002 ; Conference date: 16-09-2002 Through 18-09-2002",
}