Covariance estimation in full- and reduced-dimensionality image classification

Authors:
John A. Robinson
Affiliations:
Department of Electronics, University of York, York YO10 5DD, UK
Venue:
Image and Vision Computing
Year:
2009

Citing 15
Cited 1

Classification algorithms

Classification algorithms
Covariance Matrix Estimation and Classification With Limited Training Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Probabilistic Subspaces for Visual Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Global Dimensionality of Face Space

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Empirical Bayesian estimation of normal variances and covariances

Journal of Multivariate Analysis
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Estimation of true Mahalanobis distance from eigenvectors of sample covariance matrix

Systems and Computers in Japan
Capitalize on Dimensionality Increasing Techniques for Improving Face Recognition Grand Challenge Performance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Regularized single-kernel conditional density estimation for face description

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces an estimation technique for covariance matrices. The method differs from previous estimators in specifying an application-dependent cost function, regularizing all classes in the same way then compensating for volume distortions via scale parameters, and allowing m-fold rather than leave-one-out cross-validation. It provides a systematic basis for parameter estimation in high-dimensional spaces, where there are inevitably far too few training samples for reliable parameter estimates from sample statistics only. This is demonstrated with standard classifiers using normal models in the high dimensional space of appearance-based image processing. When the models are trained with the new technique, face classification performance is significantly better than with unregularized covariances and with earlier regularized estimators. Dimensionality reduction is also improved when it uses a covariance structure estimated with the method.