1. A method for learning a rejector by forming at least one classification tree with training images, comprising steps of: (a) acquiring, by using the rejector, p features from mk respective positive images and nk respective negative images if the mk positive images and the nk negative images are acquired with respect to a k-th node (k>=1 as an integer) of the at least one classification tree; and (b) performing, by using the rejector, processes of: (i) deciding, by using the rejector, a feature ID and a threshold by which a minimum classification error is derived by referring to a histogram of features of m1 positive images and that of features of n1 negative images if the number k is 1, wherein the feature ID is selected among the p feature IDs; and (ii) deciding, by using the rejector, a specific feature ID and a specific threshold by which a minimum classification error is derived by referring to a histogram of features of the mk positive images and that of features of the nk negative images if the number k is equal to or greater than 2 and if a number of images classified to the k-th node is larger than that classified to a brother node of the k-th node as a result of classifying images acquired with respect to a parent node of the k-th node based on a determined feature ID and a determined threshold for the parent node and then allocating the specific feature ID and the specific threshold in nodes, which have depth equal to the k-th node of the at least one classification tree.