Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning to recognize three-dimensional objects
Neural Computation
What Is the Role of Independence for Visual Recognition?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Journal of Cognitive Neuroscience
Backpropagation applied to handwritten zip code recognition
Neural Computation
Feature selection by maximum marginal diversity: optimality and implications for visual recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Minimum probability of error image retrieval
IEEE Transactions on Signal Processing
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The design of optimal feature sets for visual classification problems is still one of the most challenging topics in the area of computer vision. In this work, we propose a new algorithm that computes optimal features, in the minimum Bayes error sense, for visual recognition tasks. The algorithm now proposed combines the fast convergence rate of feature selection (FS) procedures with the ability of feature extraction (FE) methods to uncover optimal features that are not part of the original basis function set. This leads to solutions that are better than those achievable by either FE or FS alone, in a small number of iterations, making the algorithm scalable in the number of classes of the recognition problem. This property is currently only available for feature extraction methods that are either sub-optimal or optimal under restrictive assumptions that do not hold for generic imagery. Experimental results show significant improvements over these methods, either through much greater robustness to local minima or by achieving significantly faster convergence.