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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References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Bartlett, P., Pereira, F.C.N., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1106–1114 (2012)
Fujino, S., Hatanaka, T., Mori, N., Matsumoto, K.: The evolutionary deep learning based on deep convolutional neural network for the anime storyboard recognition. In: 14th International Conference Distributed Computing and Artificial Intelligence, DCAI 2017, Porto, Portugal, 21–23 June 2017, pp. 278–285 (2017)
Fujino, S., Mori, N., Matsumoto, K.: Deep convolutional networks for human sketches by means of the evolutionary deep learning. In: Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, IFSA-SCIS 2017, Otsu, Japan, 27-30 June 2017, pp. 1–5 (2017)
Takayama, K., Johan, H., Nishita, T.: Face detection and face recognition of cartoon characters using feature extraction. In: Image, Electronics and Visual Computing Workshop, p. 48 (2012)
Burie, J.-C., Nguyen, N.-V., Rigaud, C.: Comic characters detection using deep learning. In: 2nd International Workshop on coMics ANalysis, Processing and Understanding (MANPU) (2017)
Matsumoto, K., Ueno, M., Mori, N.: 2-scene comic creating system based on the distribution of picture state transition. In: Advances in Intelligent Systems and Computing, vol. 290, pp. 459–467 (2014)
Ueno, M.: Computational interpretation of comic scenes. In: Advances in Intelligent Systems and Computing, vol. 474, pp. 387–393 (2016)
Ueno, M., Mori, N., Suenaga, T., Isahara, H.: Estimation of structure of four-scene comics by convolutional neural networks. In: Proceedings of the 1st International Workshop on coMics ANalysis, Processing and Understanding, p. 9. ACM (2016)
Yunming, P., Zhining, Y.: The genetic convolutional neural network model based on random sample. Int. J. u- e-Serv. Sci. Technol. 8(11), 317–326 (2015)
Suganuma, M., Shirakawa, S., Nagao, T.: A genetic programming approach to designing convolutional neural network architectures. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 497–504. ACM (2017)
Wang, M.: Multi-path convolutional neural networks for complex image classification. CoRR, abs/1506.04701 (2015)
Fujino, H.: Compeito ! 1 (Confetti ! 1). Houbunsha (2007)
Chollet, F., et al.: Keras (2015). https://github.com/keras-team/keras
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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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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DOI: https://doi.org/10.1007/978-3-319-94649-8_17
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