The nature of statistical learning theory
The nature of statistical learning theory
Face Recognition System Using Local Autocorrelations and Multiscale Integration
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Invariant object recognition using eigenvalues of covariance matrices and autocorrelation
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Wavelet and curvelet moments for image classification: Application to aggregate mixture grading
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Invariant 2D object recognition using KRA and GRA
Expert Systems with Applications: An International Journal
PCA based Hurst exponent estimator for fBm signals under disturbances
IEEE Transactions on Signal Processing
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Revealing digital fakery using multiresolution decomposition and higher order statistics
Engineering Applications of Artificial Intelligence
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The autocorrelations have been previously used as features for 1D or 2D signal classification in a wide range of applications, like texture classification, face detection and recognition, EEG signal classification, and so on. However, in almost all the cases, the high computational costs have hampered the extension to higher orders (more than the second order). In this paper we present an effective method for using higher order autocorrelation functions for pattern recognition. We will show that while the autocorrelation feature vectors (described below) are elements of a high dimensional space, one may avoid their explicit computation when the method employed can be expressed in terms of inner products of input vectors. Different typical scenarios of using the autocorrelations will be presented and we will show that the order of autocorrelations is no longer an obstacle.