ComicLib: A New Large-Scale Comic Dataset for Sketch Understanding

Abstract

The sketch is essential in everyday communication and has received much attention in the computer vision community. In general, researchers use learning-based approaches to study sketch-based algorithms. These methods rely on large-scale data to train complex models to achieve satisfactory performance. Most existing datasets are drawn by unskilled users in a closed environment. These datasets are of low complexity, making deep learning models unable to extract more information. This paper proposes a new large-scale comic sketch dataset called ComicLib for sketch understanding. We scan 181,354 comic sketch images from the comic library and annotate them through a crowdsourcing annotation platform developed by ourselves. Finally, we obtain a dataset of millions of comic objects in 17 categories. We conduct comparative experiments on sketch recognition, retrieval, detection, generation and colorization using a number of deep learning algorithms. These experiments provide the benchmark performance of the ComicLib dataset. We hope that ComicLib can contribute to the field of sketch-based research.