The nature of statistical learning theory
The nature of statistical learning theory
An application of wavelet-based affine-invariant representation
Pattern Recognition Letters
Machine Learning
Wavelet-Based Affine Invariant Representation: A Tool for Recognizing Planar Objects in 3D Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of 2D Object Contours Using the Wavelet Transform Zero-Crossing Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Invariant 2D object recognition using the wavelet modulus maxima
Pattern Recognition Letters
Feature extraction using wavelet and fractal
Pattern Recognition Letters
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Rotation invariant feature extraction using Ridgelet and Fourier transforms
Pattern Analysis & Applications
Shift invariant properties of the dual-tree complex wavelet transform
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Digital curvelet transform for palmprint recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Robust support vector machine with bullet hole image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image Denoising Using Three Scales of Wavelet Coefficients
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Mathematics and Computers in Simulation
Palmprint verification using binary orientation co-occurrence vector
Pattern Recognition Letters
Invariant pattern recognition using contourlets and AdaBoost
Pattern Recognition
Three-dimensional imaging information acquisition system based on DSP and SVM
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Palmprint recognition combining LBP and cellular automata
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Orthogonal locally discriminant projection for palmprint recognition
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Evolution strategies based adaptive Lp LS-SVM
Information Sciences: an International Journal
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory
Computers and Electrical Engineering
Expert Systems with Applications: An International Journal
On-line fast palmprint identification based on adaptive lifting wavelet scheme
Knowledge-Based Systems
Circular projection for pattern recognition
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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A novel descriptor for pattern recognition is proposed by using dual-tree complex wavelet features and SVM. The approximate shift-invariant property of the dual-tree complex wavelet and its good directional selectivity in 2D make it a very appealing choice for pattern recognition. Recently, SVM has been shown to be very successful in pattern recognition. By combining these two tools we find that better recognition results are obtained. We achieve the highest rates when we use the dual-tree complex wavelet features with the Gaussian radial basis function kernel and the wavelet kernel for recognizing similar handwritten numerals, and when we use the Gaussian radial basis function for palmprint classification. Our findings are that the dual-tree complex wavelets are always better than the scalar wavelet for pattern recognition when SVM is used. Also, among many frequently used SVM kernels, the Gaussian radial basis function kernel and the wavelet kernel are the best for pattern recognition applications.