Dissimilarity computation through low rank corrections

Authors:
Dorin Comaniciu;Peter Meer;David Tyler
Affiliations:
Imaging and Visualization Department, Siemens Corporate Research, Inc., 755 College Road East, Princeton, NJ;Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ;Department of Statistics, Rutgers University, Piscataway, NJ
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

Most of the energy of a multivariate feature is often contained in a low dimensional subspace. We exploit this property for the efficient computation of a dissimilarity measure between features using an approximation of the Bhattacharyya distance. We show that for normally distributed features the Bhattacharyya distance is a particular case of the Jensen-Shannon divergence, and thus evaluation of this distance is equivalent to a statistical test about the similarity of the two populations. The accuracy of the proposed approximation is tested for the task of texture retrieval.