Structured Covariance Matrices for Statistical Image Object Recognition

Abstract. In this paper we present di erent approaches to structuring covariance matrices within statistical classi ers. This is motivated by the fact that the use of full covariance matrices is infeasible in many applications. On the one hand, this is due to the high number of model parameters that have to be estimated, on the other hand the computational complexity of a classi er based on full covariance matrices is very high. We propose the use of diagonal and band-matrices to replace full covariance matrices and we also show that computation of tangent distance is equivalent to using a structured covariance matrix within a statistical classi er.

• Type: Conference
• Year: 2000
• Where: DAGM
• Authors: Jörg Dahmen, Daniel Keysers, Michael Pitz, Hermann Ney
• Keywords: Classi Er, Covariance Matrices, DAGM 2000, Image Processing, Statistical Classi Er