Analysis of Partial Semantic Segmentation for Images of Four-Scene Comics
Analysis of Partial Semantic Segmentation for Images of Four-Scene Comics
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DOI：
10.1007/978-3-030-53036-5_6
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Akira Terauchi;N. Mori;Miki Ueno
中科院分区：
文献类型：
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作者：
Akira Terauchi;N. Mori;Miki Ueno
在人工智能（AI）的帮助下理解人类创造物的方式已经增加;然而，这些仍然被认为是最困难的任务之一。我们的研究挑战是找到通过人工智能理解四场景漫画的方法。为了实现这一目标，我们使用了一个名为“四场景漫画故事数据集”的新数据集，这是研究人员和漫画艺术家为开发AI创作而制作的第一个数据集。在本文中，我们专注于部分语义分割的特征，如眼睛，嘴，或讲话气球。由于缺乏标注的漫画数据集，漫画的语义分割任务一直很困难。为了解决这个问题，我们利用了数据集的特性，轻松地创建了带注释的数据集。对于语义分割方法，我们使用了一个名为DeepLabv 3+的模型。通过对四场景漫画图像的分割实验，验证了该方法的有效性。
Ways of understanding human creations with the help of artificial intelligence (AI) have increased; however, those are still known as being one of the most difficult tasks. Our research challenge is to find ways to understand four-scene comics through AI. To achieve this aim, we used a novel dataset called “Four-scene Comics Story Dataset”, which is the first dataset made by researchers and comic artists to develop AI creations. In this paper, we focused on the partial semantic segmentation of features such as eyes, mouth, or speech balloons. The semantic segmentation task of comics has been difficult because of the lack of annotated comic dataset. To solve this problem, we utilized the features of our dataset and easily created annotated dataset. For the semantic segmentation method, we used a model called DeepLabv3+. The effectiveness of our experiment is confirmed by computer simulations showing the segmentation result of test images from four-scene comics.