Invariant image object recognition using mixture densities
Jörg Dahmen,Daniel Keysers,Mark Oliver Güld,Hermann Ney +3 more
- 03 Sep 2000
- Vol. 2, pp 614-617
TL;DR: 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, are proposed, which obtains an excellent classification error rate on the US Postal Service handwritten digits recognition task.
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Abstract: 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.
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Citations
Experiments with an extended tangent distance
Daniel Keysers,Jörg Dahmen,T. Theiner,Hermann Ney +3 more
- 01 Sep 2000
TL;DR: An extended tangent distance is incorporated in a kernel density based Bayesian classifier to compensate for affine image variations and an image distortion model for local variations is introduced.
Statistical Image Object Recognition using Mixture Densities
TL;DR: A mixture density based approach to invariant image object recognition, an approach to estimating covariance matrices with respect to image variabilities as well as a new approach to combined classification, called the virtual test sample method are proposed.
35
A Probabilistic View on Tangent Distance
Daniel Keysers,Jörg Dahmen,Hermann Ney +2 more
- 13 Sep 2000
TL;DR: A new probabilistic interpretation of tangent distance is presented, which proved to be very effective in modeling image transformations in object recognition and a possible generalization is presented.
•Dissertation
Modeling of image variability for recognition
Daniel Martin Keysers
- 01 Jan 2006
TL;DR: This thesis presents the application of different models of image variability to visual recognition problems using the paradigm of appearance-based recognition and describes a model for holistic scene analysis that allows to determine a visual representation of objects present in a set of images.
14
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Experiments with a new boosting algorithm
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