Structured Covariance Matrices for Statistical Image Object Recognition
نویسندگان
چکیده
In this paper we present diierent approaches to structur-ing covariance matrices within statistical classiiers. 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 classiier 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 classiier.
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