Estimation of structure of four-scene comics by convolutional neural networks
Miki Ueno, Naoki Mori, Toshinori Suenaga, Hitoshi Isahara · 2016
The computational interpretation of comics is one of the important topics being studied in the field of artificial intelligence and image recognition. There are a lot of challenging tasks to undertake in order to interpret comics, i.e., recognize objects in gray-scaled drawing image, extract emotional information of scenes, and define models of continuous scenes by considering the structure of comics. In this paper, we focused on four scene comics and their transition. Four-scene comics have a structure which originated in four-part of Chinese-poetry so creators clearly draw the semantic distance between each scene. It is very important for expressing the interesting and lyrical aspects of comics. To detect the transition of scenes, convolutional neural networks(CNNs) are constructed and computer experiments were carried out. The results suggest that CNN is able to detect the transition of scenes and that the features of each scene are quite different.