Abstract
Effectively solving the problem of sketch generation, which aims to produce human-drawing-like sketches from real photographs, opens the door for many vision applications such as sketch-based image retrieval and non-photorealistic rendering. In this paper, we approach automatic sketch generation from a human visual perception perspective. Instead of gathering insights from photographs, for the first time, we extract information from a large pool of human sketches. In particular, we study how multiple Gestalt rules can be encapsulated into a unified perceptual grouping framework for sketch generation. We further show that by solving the problem of Gestalt confliction, i.e., encoding the relative importance of each rule, more similar to human-made sketches can be generated. For that, we release a manually labeled sketch dataset of 96 object categories and 7680 sketches. A novel evaluation framework is proposed to quantify human likeness of machine-generated sketches by examining how well they can be classified using models trained from human data. Finally, we demonstrate the superiority of our sketches under the practical application of sketch-based image retrieval. © 2015 Elsevier B.V.