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Recognizing the Order of Four-Scene Comics by Evolutionary Deep Learning

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Distributed Computing and Artificial Intelligence, 15th International Conference (DCAI 2018)

Abstract

In recent years, comic analysis has become an attractive research topic in the field of artificial intelligence. In this study, we focused on the four-scene comics and applied deep convolutional neural networks (DCNNs) to the data for understanding the order structure. The tuning of the DCNN hyperparameters requires considerable effort. To solve this problem, we propose a novel method called evolutionary deep learning (evoDL) by means of genetic algorithms. The effectiveness of evoDL is confirmed by an experiment conducted to identify structural problems in actual four-scene comics.

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Acknowledgments

A part of this work was supported by JSPS KAKENHI Grant, Grant-in-Aid for Scientific Research(C), 26330282. A part of this work was supported by LEAVE A NEST CO., LTD. I would like to thank FUJINO HARUKA for providing her comic book as the dataset. The authors would like to acknowledge the helpful discussions with Dr. Miki Ueno of Toyohashi University of Technology, Japan.

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Correspondence to Saya Fujino .

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Fujino, S., Mori, N., Matsumoto, K. (2019). Recognizing the Order of Four-Scene Comics by Evolutionary Deep Learning. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_17

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