Domain separation for efficient adaptive active learning

This paper proposes a procedure aimed at efficiently adapting a classifier trained on a source image to a similar target image. The adaptation is carried out through active queries in the target domain following a strategy particularly designed for the case where class distributions have shifted between the two images. We first suggest a pre-selection of candidate pixels issued from the target image by keeping only those samples appearing to be lying in a region of the input space not yet covered by the existing ground truth (source domain pixels). Then, exploiting a classifier integrating instance weights, active queries are performed on the target image. As the inclusion to the training set of the samples progresses, the weights associated with the training pixels are updated using different criteria according to their origin (source or target domain). Experiments on a pair of QuickBird images of urban scenes prove the validity of the proposed approach if compared to existing benchmark methods.