Invariant image object recognition using mixture densities

We present a mixture density based approach to invariant image object recognition. We start our experiments using Gaussian mixture densities within a Bayesian classifier. Invariance to affine transformations is achieved by replacing the Euclidean distance with SIMARD´s tangent distance. We propose an approach to estimating covariance matrices with respect to image invariances as well as a new classifier combination scheme, called the virtual test sample method. On the US Postal Service handwritten digits recognition task (USPS), we obtain an excellent classification error rate of 2.7%, using the original USPS training and test sets