1. A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein the subset comprises at least one image and the positive images include at least one object of the same type as the object to be detected; (c) training one or more weak classifiers based on at least one candidate exemplar from the set of candidate exemplars, the training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the at least one candidate exemplar, wherein the training comprises clustering the plurality of heterogeneous compositional features and the corresponding heterogeneous compositional features about a set of centers to obtain a set of composite heterogeneous feature clusters and encoding spatial relationships between each of the composite heterogeneous feature clusters in the set of composite heterogeneous feature clusters to produce an exemplar based geometric template; and (d) combining the one or more weak classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.