J. Dahmen, D. Keysers, M. Pitz, and H. Ney. Structured Covariance Matrices for Statistical Image Object Recognition. In 22. DAGM Symposium Mustererkennung 2000, Springer, Kiel, Germany, September 2000. This volume. Home/Search Document Details and Download
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Manifold Parzen Windows - Vincent, Bengio (2002) (4 citations) (Correct)
.... algorithm [7] While this approach was initially aimed at solving classification tasks with a nearest neighbor paradigm, some work has already been done in developing it into a probabilistic interpretation for mixtures with a few gaussians, as well as for full fledged kernel density estimation [8, 9]. The main difference between our approach and the above is that the Manifold Parzen estimator does not require prior knowledge, as it infers the local directions directly from the data, although it should be easy to also incorporate prior knowledge if available. We should also mention ....
J. Dahmen, D. Keysers, M. Pitz, and H. Ney. Structured covariance matrices for statistical image object recognition. In 22nd Symposium of the German Association for Pattern Recognition, Kiel, Germany, 2000.
A Probabilistic View on Tangent Distance - Keysers, Dahmen, Ney (2000) (1 citation) Self-citation (Dahmen Keysers Ney) (Correct)
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J. Dahmen, D. Keysers, M. Pitz, and H. Ney. Structured Covariance Matrices for Statistical Image Object Recognition. In 22. DAGM Symposium Mustererkennung 2000, Springer, Kiel, Germany, September 2000. This volume.
Statistical Image Object Recognition using Mixture Densities - Dahmen, Keysers, Ney, Güld (2000) (4 citations) Self-citation (Dahmen Keysers Ney) (Correct)
....the parameters (with = 2 I in the experiments) Note that T = 1 N Z p( N X n=1 x n; d = 18) Thus, the empirical sample mean does not change in the presence of tangent vectors. More information on the probabilistic interpretation of tangent distance can be found in [4, 16]. 4.3. The image distortion model Computation of tangent distance as given in Eq. 13) still requires the calculation of the (squared) Euclidean distance between the optimally transformed image x and the reference image . Although small global transformations have been compensated for by the ....
J. Dahmen, D. Keysers, M. Pitz, and H. Ney, \Structured covariance matrices for statistical image object recognition", in Proc. 22nd Symposium German Association for Pattern Recognition, Kiel, Germany, 2000, pp. 99-106.
Improving Automatic Speech Recognition Using Tangent.. - Macherey, Keysers.. (2001) Self-citation (Dahmen Keysers Ney) (Correct)
.... ) i (11) Note that the exponent in Eq. 11) leads to conventional Mahalanobis distance for 0 and TD for 1. Thus, the incorporation of tangent vectors adds a corrective term to the Mahalanobis distance that only affects the covariance matrix which can be interpreted as structuring [7]. 3. Incorporating TD into ASR In the last section the assumption was made that the transformations for which invariance is desired are known. However, in contrast to most image object recognition tasks, the transformations to be selected are not obvious in ASR and often there is no prior ....
J. Dahmen, D. Keysers, M. Pitz, and H. Ney, "Structured covariance matrices for statistical image object recognition, " in 22. DAGM Symposium Mustererkennung 2000, (Kiel, Germany), pp. 99--106, Springer, Sept. 2000.
A Probabilistic View on Tangent Distance - Keysers, Dahmen, Ney (2000) (1 citation) Self-citation (Dahmen Keysers Ney) (Correct)
....to factor analysis as a resort [6] This problem can be circumvented by regarding the distribution in the space originating from projection along the subspace. Note that the presented considerations can be interpreted as imposing a certain structure on the covariance matrix due to tangent distance [2]. 3.2 Estimating derivatives of variation in the reference In some cases there is no a priori information available about the directions of variation of the data to be modeled, but it is known that there exists class speci c variability in the data. In this case one needs to estimate the ....
J. Dahmen, D. Keysers, M. Pitz, and H. Ney. Structured Covariance Matrices for Statistical Image Object Recognition. In 22. DAGM Symposium Mustererkennung 2000, Springer, Kiel, Germany, September 2000. This volume.
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