ART2CONTOUR: SALIENT CONTOUR DETECTION IN ARTWORKS USING GENERATIVE ADVERSARIAL NETWORKS

Artists or art workshops often reuse their motifs directly or in a slightly amended form. To allow a better comparison of these artworks, salient contours are extracted that reduce them to the most important lines or boundaries. For this task, we propose a generative adversarial network (GAN) based approach to learn the mapping from artwork images to contour drawings in a supervised manner. We introduce the combination of multiple regression task losses to encourage the learning of salient contours. For the evaluation, we created a dataset of high-resolution prints and paintings and corresponding annotated ground truth drawings. We show that our method visually and quantitatively outperforms competing methods in contour detection on prints and paintings.