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ICPR
2000
IEEE

Invariant Image Object Recognition Using Mixture Densities

9 years 7 months ago
Invariant Image Object Recognition Using Mixture Densities
In this paper 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 ? ?, using the original USPS training and test sets.
Daniel Keysers, Hermann Ney, Jörg Dahmen, Mar
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2000
Where ICPR
Authors Daniel Keysers, Hermann Ney, Jörg Dahmen, Mark Oliver Güld
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