ROBUST ESTIMATION OF CORRELATION WITH APPLICATIONS TO COMPUTER VISION

In this paper we compare to the standard correlation coefficient three estimators of similarity for visual patterns which are based on the L(2) and L(1) norms. The emphasis of the comparison is on the stability of the resulting estimates. Bias, efficiency, normality and robustness are investigated through Monte Carlo simulations in a statistical task, the estimation of the correlation parameter of a binormal distribution. The four estimators are then compared on two pattern recognition tasks: people identification through face recognition and book identification from the cover image. The similarity measures based on the L(1) norm prove to be less sensitive to noise and provide better performance than those based on L(2) norm.