Learning Graphs to Model Visual Objects Across Different Depictive Styles

Abstract. Visual object classification and detection are major prob-lems in contemporary computer vision. State-of-art algorithms allow t-housands of visual objects to be learned and recognized, under a wide range of variations including lighting changes, occlusion, point of view and different object instances. Only a small fraction of the literature ad-dresses the problem of variation in depictive styles (photographs, draw-ings, paintings etc.). This is a challenging gap but the ability to process images of all depictive styles and not just photographs has potential val-ue across many applications. In this paper we model visual classes using a graph with multiple labels on each node; weights on arcs and nodes indicate relative importance (salience) to the object description. Visual class models can be learned from examples from a database that contains photographs, drawings, paintings etc. Experiments show that our repre-sentation is able to improve upon Deformable Part Models for detection and Bag of Words models for classification