Details

Autor(en) / Beteiligte

• Tuzel, Oncel
• Porikli, Fatih
• Meer, Peter

Titel

Region Covariance: A Fast Descriptor for Detection and Classification

Ist Teil von

• Computer Vision – ECCV 2006, 2006, p.589-600

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2006

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.