In this paper we propose a new classifier - the Maximal Rejection Classifier (MRC) - for target detection. Unlike pattern recognition, pattern detection problems require a separation between two classes, Target and Clutter, where the probability of the former is substantially smaller, compared to that of the latter. The MRC is a linear classifier, based on successive rejection operations. Each rejection is performed using a projection followed by thresholding. The projection vector is designed to maximize the number of rejected Clutter inputs. It is shown that it also minimizes the expected number of operations until detection. An application of detecting frontal faces in images is demonstrated using the MRC with encouraging results.
['Michael Elad', 'Yacov Hel-Or', 'Renato Keshet']
Pattern Detection Using a Maximal Rejection Classifier