Method of automatically training a classifier hierarchy by dynamic grouping the training samples

摘要

The present invention uses dynamic grouping to divide up training samples to train different classification nodes. At the beginning of the training, all samples are in the same group. A clustering process is applied in the feature space of the selected feature vectors with cluster indexes accumulated. The average of all the accumulated cluster indexes is used as the threshold for splitting the samples into two groups. When the splitting criterion is met, samples are split into two groups based on their similarity in the feature space.

主权项

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; andrepeat steps (a)-(d) until a stopping threshold is met; wherein the splitting criterion is:fTFA>δ where f denotes a false alarm rate of a current ensemble classifier, TFA denotes a target false alarm rate, and δ≧1 is a threshold controlling a tolerance of non-perfect convergence.