Pattern recognition using higher-order local autocorrelation coefficients

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). We present a method which avoids the computation of the autocorrelation coefficients and which can be applied to a large set of tools commonly used in statistical pattern recognition. We discuss different scenarios of using the autocorrelations and we show that the order of autocorrelations is no longer an obstacle.