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.
Return to Research: Content-based retrieval        Return to List of Publications
Download the paper