Region Covariance: A Fast Descriptor for
Detection and Classification.
Oncel Tuzel (1,3) Fatih Porikli (3) and Peter Meer (1,2)
(1)Department of Computer Science
(2)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(3) Mitsubishi Electric Research Laboratories
Cambridge, MA 02139
We describe a new region descriptor and apply it to two
problems, object detection and texture classification. The covariance of
d-features, e.g., the three-dimensional color vector, the norm of first and
second derivatives of intensity with respect to x and y, etc., characterizes
a region of interest. We describe a fast method for computation of covariances
based on integral images. The idea presented here is more general
than the image sums or histograms, which were already published before,
and with a series of integral images the covariances are obtained by a
few arithmetic operations. Covariance matrices do not lie on Euclidean
space, therefore we use a distance metric involving generalized eigenvalues
which also follows from the Lie group structure of positive definite
matrices. Feature matching is a simple nearest neighbor search under
the distance metric and performed extremely rapidly using the integral
images. The performance of the covariance features is superior to other
methods, as it is shown, and large rotations and illumination changes are
also absorbed by the covariance matrix.
9th European Conference on Computer Vision
, Graz, Austria, May 2006, vol. II, 589-600.
