Effective constructing training sets for object detection

This paper addresses the problem of building up effective training sets at minimal labeling cost for object detection. This problem occurs in the situation that the part-based detector is trained on a group of positive examples with bounding box labels, but the images selected by uniform sampling do not reflect the desired training distribution and need additional labeling cost in order to obtain enough positive examples. We study the active training process in which some object windows are sampled from a pool of unlabeled candidate windows, and then their corresponding bounding annotations are queried. We derive an effective training set by selecting a group of most uncertain object windows according to the current detector. Our approach has been empirically demonstrated on the object detection task of PASCAL VOC dataset. The experiment results show that our proposed algorithm outperforms common uniform sampling within the same labeling cost.