Confidence-Rated Multiple Instance Boosting for Object Detection

Over the past years, Multiple Instance Learning (MIL) has proven to be an effective framework for learning with weakly labeled data. Applications of MIL to object detection, however, were limited to handling the uncertainties of manual annotations. In this paper, we propose a new MIL method for object detection that is capable of handling the noisier automatically obtained annotations. Our approach consists in first obtaining confidence estimates over the label space and, second, incorporating these estimates within a new Boosting procedure. We demonstrate the efficiency of our procedure on two detection tasks, namely, horse detection and pedestrian detection, where the training data is primarily annotated by a coarse area of interest detector. We show dramatic improvements over existing MIL methods. In both cases, we demonstrate that an efficient appearance model can be learned using our approach.