Object Detection for Comics using Manga109 Annotations
Abstract
With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there are two problems in applying these methods to the comic object detection task. First, there is no large-scale annotated comics dataset. The CNN-based methods require large-scale annotations for training. Secondly, the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve these problems, we proposed a new annotation dataset and a new CNN model. We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations. For the assignment problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods using Manga109-annotations and confirmed that our model outperformed them based on the mAP score.
Cite
@article{arxiv.1803.08670,
title = {Object Detection for Comics using Manga109 Annotations},
author = {Toru Ogawa and Atsushi Otsubo and Rei Narita and Yusuke Matsui and Toshihiko Yamasaki and Kiyoharu Aizawa},
journal= {arXiv preprint arXiv:1803.08670},
year = {2018}
}
Comments
http://www.manga109.org/en/