Superparamagnetic clustering of data: Application to computer vision

Eytan Domany, Marcelo Blatt, Yoram Gdalyahu, Daphna Weinshall

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations

Abstract

The aim of clustering is to partition data according to natural classes present in it. We proposed recently a method that makes no explicit assumption about the structure of the data and under very general and natural assumptions solves the clustering problem by evaluating thermal properties of a disordered (granular) magnet. The method was tested successfully on a variety of artificial and real-life problems; here we emphasize its application to analyze results obtained by a novel method of computer vision. The combination of these two techniques provides a powerful tool that succeeded to cluster properly 90 images of 6 objects on the basis of their pairwise dissimilarities. These dissimilarities, which constitute a highly non-metric set of pairwise distances between the images, form the input for clustering. A hierarchical organization of the images that agrees with human intuition, was obtained without assigning to the images coordinates in some abstract space.

Original languageAmerican English
Pages (from-to)5-12
Number of pages8
JournalComputer Physics Communications
Volume121
DOIs
StatePublished - Sep 1999
EventProceedings of the 1998 Europhysics Conference on Computational Physics (CCP 1998) - Granada, Spain
Duration: 2 Sep 19985 Sep 1998

Bibliographical note

Funding Information:
We thank Shai Wiseman, Gaddy Getz and Noam Shental for most pleasant collaborations. The fMRI work required considerable patience on the part of Kalanit Grill-Spector and Rafi Malach. Discussions with several colleagues are warmly acknowledged; Ido Kanter, Michele Vendruscolo and David Mukamel in particular. This research was supported in part by the GIF – Germany Israel Science Foundation.

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