11. The method of claim 1 , further comprising: merging of trained classes by an agglomerative clustering.
1. A method for training a multi-class classifier, comprising the steps of: selecting a query image from a set of active images based on a membership probability determined by the classifier, wherein the active images are unlabeled; selecting a sample image from a set of training image based on the membership probability of the query image, wherein the training images are labeled; displaying the query image and the sample images to a user on an output device; obtaining a response from the user with an input device, wherein the response is a yes-match or a no-match; adding the query image with the label of the sample image to the training set if the yes-match is obtained, and otherwise repeating the selecting, displaying, and obtaining steps until a predetermined number of no-match is reached to obtain the multi-class classifier; applying the multi-class classifier to a set of unlabeled images to obtain a set of detection results; determining membership probabilities of the set of detection results; associating the set of detecting results with the membership probabilities less than a predetermined threshold with the set of active images; and retraining the multi-class classifier to refine the set of detection results, wherein steps of the method are performed by a processor.