Region Covariance: A Fast Descriptor for Detection and Classification

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 me...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Oncel Tuzel, Fatih Porikli, Peter Meer
• Keywords: Computer Vision, Covariance Features, Covariance Matrices, Covariance Matrix, ECCV 2006, Integral Images, Positive Definite Matrices