1. A method for training an object classifier hierarchy from a set of sample images, comprising: inputting a set of object image samples; and using a processor to: iteratively train an object classifier hierarchy as follows: (a) examine the object image samples in a feature space to extract feature vectors; (b) apply a clustering algorithm to the object image samples in the feature space of selected feature vectors; (c) split the object image samples into two groups using a splitting criterion based on the result of the application of the clustering algorithm; (d) assign one of the two groups of object image samples to train one node of the object classifier hierarchy and assign another of the two groups of object image samples to train another node of the object classifier hierarchy; and repeat steps (a)-(d) until a stopping threshold is met; wherein the splitting criterion is: f T FA > δ where f denotes a false alarm rate of a current ensemble classifier, T FA denotes a target false alarm rate, and δ≧1 is a threshold controlling a tolerance of non-perfect convergence.