1. A labeling system comprising: an electronic data processing device configured to label an image to be labeled belonging to a target domain by operations including: training a boost classifier ƒ T (x)=Σ r=1 M β r h r (x) to classify an image belonging to the target domain and represented by a feature vector x, the training using a target domain training set D T comprising labeled feature vectors representing images belonging to the target domain and a plurality of source domain training sets D S 1 ,. .. , D S N where N≧2 comprising labeled feature vectors representing images belonging to source domains S 1 ,. .. , S N respectively, the training comprising applying an adaptive boosting (AdaBoost) algorithm to generate the base classifiers h r (x) and the base classifier weights β r of the boost classifier ƒ T (x), wherein the r th iteration of the AdaBoost algorithm includes (i) performing N sub-iterations in which the k th sub-iteration trains a candidate base classifier h r k (x) on a training set combining the target domain training set D T and the source domain training set D S k and (ii) selecting h r (x) as the candidate base classifier with lowest error for the target domain training set D T ; computing a feature vector x in representing the image to be labeled; and generating a label for the image to be labeled by operations including evaluating ƒ T (x in )=Σ r=1 M β r h r (x in ).