Dissimilarity Computation through Low Rank Corrections
Dorin Comaniciu(1) Peter Meer(2) David Tyler(3)
(1)Imaging Research Department
Siemens Corporate Research
Princeton, NJ 08540
(2)Department of Electrical and Computer Engineering
(3)Department of Statistics
Rutgers University, Piscataway, NJ 08855, USA
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.
Appeared in
Pattern Recognition Letters, 24, 227-236, 2003.
Earlier version appeared in
IEEE Workshop on Content-based Access of Image
and Video Libraries (CBAIVL-99),
Fort Collins, CO, June 1999, 50-54.
