Results 1  10
of
42
Joint manifold distance: a new approach to appearance based clustering
 IEEE Conf. Computer Vision and Pattern Recognition (CVPR
, 2003
"... ..."
(Show Context)
Tangent Distance Kernels for Support Vector Machines
 IN PROCEEDINGS OF THE 16TH ICPR
, 2002
"... When dealing with pattern recognition problems one encounters different types of apriori knowledge. It is important to incorporate such knowledge into the classification method at hand. A very common type of apriori knowledge is transformation invariance of the input data, e.g. geometric transform ..."
Abstract

Cited by 41 (9 self)
 Add to MetaCart
When dealing with pattern recognition problems one encounters different types of apriori knowledge. It is important to incorporate such knowledge into the classification method at hand. A very common type of apriori knowledge is transformation invariance of the input data, e.g. geometric transformations of imagedata like shifts, scaling etc. Distance based classification methods can make use of this by a modified distance measure called tangent distance [13, 14]. We introduce a new class of kernels for support vector machines which incorporate tangent distance and therefore are applicable in cases where such transformation invariances are known. We report experimental results which show that the performance of our method is comparable to other stateoftheart methods, while problems of existing ones are avoided.
Statistical Framework for Modelbased Image Retrieval in . . .
, 2003
"... Recently, research in the field of contentbased image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical ap ..."
Abstract

Cited by 39 (9 self)
 Add to MetaCart
Recently, research in the field of contentbased image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical applications. The concept for contentbased image retrieval in medical applications (IRMA) is therefore based on the separation of the following processing steps: categorization of the entire image; registration with respect to prototypes; extraction and querydependent selection of local features; hierarchical blob representation including object identification; and finally, image retrieval. Within the first step of processing, images are classified according to image modality, body orientation, anatomic region, and biological system. The statistical classifier for the anatomic region is based on Gaussian kernel densities within a probabilistic framework for multiobject recognition. Special emphasis is placed on invariance, employing a probabilistic model of variability based on tangent distance and an image distortion model. The performance of the classifier is evaluated using a set of 1617 radiographs from daily routine, where the error rate of 8.0% in this sixclass problem is an excellent result, taking into account the difficulty of the task. The computed posterior probabilities are furthermore used in the subsequent steps of the retrieval process.
Elastic Image Matching is NPComplete
 Pattern Recognition Letters
, 2003
"... One fundamental problem in image recognition is to establish the resemblance of two images. This can be done by searching the best pixel to pixel mapping taking into account monotonicity and continuity constraints. We show that this problem is NPcomplete by reduction from 3SAT, thus giving evidence ..."
Abstract

Cited by 28 (3 self)
 Add to MetaCart
(Show Context)
One fundamental problem in image recognition is to establish the resemblance of two images. This can be done by searching the best pixel to pixel mapping taking into account monotonicity and continuity constraints. We show that this problem is NPcomplete by reduction from 3SAT, thus giving evidence that the known exponential time algorithms are justi ed, but approximation algorithms or simpli cations are necessary.
Handwritten Digit Classification using Higher Order Singular Value Decomposition
"... In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm use ..."
Abstract

Cited by 21 (1 self)
 Add to MetaCart
In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm uses the HOSVD for tensor approximation simultaneously in two modes. Classification results for the second algorithm are almost down at 5 % even though the approximation reduces the original training data with more than 98 % before the construction of the class models. The actual classification in the test phase for both algorithms is conducted by solving a series least squares problems. Considering computational amount for the test presented the second algorithm is twice as efficient as the first one.
Maximum Entropy and Gaussian Models for Image Object Recognition
 In Pattern Recognition, 24th DAGM Symposium
, 2002
"... The principle of maximum entropy is a powerful framework that can be used to estimate class posterior probabilities for pattern recognition tasks. In this paper, we show how this principle is related to the discriminative training of Gaussian mixture densities using the maximum mutual information cr ..."
Abstract

Cited by 18 (9 self)
 Add to MetaCart
(Show Context)
The principle of maximum entropy is a powerful framework that can be used to estimate class posterior probabilities for pattern recognition tasks. In this paper, we show how this principle is related to the discriminative training of Gaussian mixture densities using the maximum mutual information criterion. This leads to a relaxation of the constraints on the covariance matrices to be positive (semi)definite.
Local Context in Nonlinear Deformation Models for Handwritten Character Recognition
 Proc. Int’l Conf. Pattern Recognition
, 2004
"... We evaluate different twodimensional nonlinear deformation models for handwritten character recognition. Starting from a true twodimensional model, we derive pseudotwodimensional and zeroorder deformation models. Experiments show that it is most important to include suitable representations of ..."
Abstract

Cited by 18 (9 self)
 Add to MetaCart
(Show Context)
We evaluate different twodimensional nonlinear deformation models for handwritten character recognition. Starting from a true twodimensional model, we derive pseudotwodimensional and zeroorder deformation models. Experiments show that it is most important to include suitable representations of the local image context of each pixel to increase performance. With these methods, we achieve very competitive results across five different tasks, in particular 0.5 % error rate on the MNIST task. 1
Learning of variability for invariant statistical pattern recognition
 In ECML 2001, 12th European Conference on Machine Learning
, 2001
"... ..."
(Show Context)
Minimum distance between pattern transformation manifolds: Algorithm and applications
 IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—Transformation invariance is an important property in pattern recognition, where different observations of the same object typically receive the same label. This paper focuses on a transformationinvariant distance measure that represents the minimum distance between the transformation mani ..."
Abstract

Cited by 18 (12 self)
 Add to MetaCart
(Show Context)
Abstract—Transformation invariance is an important property in pattern recognition, where different observations of the same object typically receive the same label. This paper focuses on a transformationinvariant distance measure that represents the minimum distance between the transformation manifolds spanned by patterns of interest. Since these manifolds are typically nonlinear, the computation of the manifold distance (MD) becomes a nonconvex optimization problem. We propose representing a pattern of interest as a linear combination of a few geometric functions extracted from a structured and redundant basis. Transforming the pattern results in the transformation of its constituent parts. We show that, when the transformation is restricted to a synthesis of translations, rotations, and isotropic scalings, such a pattern representation results in a closedform expression of the manifold equation with respect to the transformation parameters. The MD computation can then be formulated as a minimization problem whose objective function is expressed as the difference of convex functions (DC). This interesting property permits optimally solving the optimization problem with DC programming solvers that are globally convergent. We present experimental evidence which shows that our method is able to find the globally optimal solution, outperforming existing methods that yield suboptimal solutions. Index Terms—Transformation invariance, pattern manifolds, sparse approximations. Ç 1