Search

Many algorithms in computer vision, e.g., for object localization, are supervised and need annotated training data. One approach for object localization is the Discriminative Generalized Hough Transform (DGHT). It achieves state-of-the-art performance in applications like iris and epiphysis localization, if the amount and quality of training data is sufficient. This motivates techniques for extending the training corpus with limited manual effort. In this paper, we propose an active learning scheme to extend the training corpus by automatically and efficiently harvesting and selecting suitable Web images. We aim at improving localization performance, while reducing the manual supervision to a minimum. Our key idea is to estimate the benefit of a particular candidate Web image by analyzing its Hough space generated using an initial DGHT model. We show that our method performs similarly to a manual selection of Web images as well as a computationally intensive state-of-the-art approach.