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Structure Analysis on Common Plot in Four-Scene Comic Story Dataset

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11296))

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Abstract

Comic is the one of the most attractive creative contents and it contains both components of image and words features. Especially, I have been focused in four-scene comics which can represent stories with the simple and clear structure. One of my aims of the researches is to promote collaboration between creators and artificial intelligence. To contribute for the field, I have proposed the original four-scene comics dataset with creative process and meta-data. According to the existing comics, I defined the typical patterns of structure and contents. I provided the character and several information to keep balance of common twenty scenarios based on two types of structure for ten plots. The dataset contains 100 kinds of four-scene comics to keep layer information and several annotations by five artists. Thus, it can be analyzed various expressions in common scenarios. In this research, I show the procedure of creating the dataset. Then, I describe the features of the dataset and results of computational experiment.

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References

  1. Ueno, M., Fukuda, K., Mori, N.: Can Computers Create Comics and Animations?, Computational and Cognitive Approaches to Narratology, pp. 164–190 (2016)

    Google Scholar 

  2. Matsushita, M., et al.: My Book Mark Comic Engineering. Artificial Intelligence, vol. 32 (2017). Advances in Intelligent Systems and Computing, vol. 290, pp. 459–467. Springer (2014)

    Google Scholar 

  3. Ueno, M., Isahara, H.: Relationships between features and story description in comics. In: The 30th Annual Conference of the Japanese Society for Artificial Intelligence, 2J5-OS-08b-4in2 (2016)

    Google Scholar 

  4. Ueno, M., Mori, N., Matsumoto, K.: 2-scene comic creating system based on the distribution of picture state transition. In: Omatu, S., Bersini, H., Corchado, J.M., Rodríguez, S., Pawlewski, P., Bucciarelli, E. (eds.) Distributed Computing and Artificial Intelligence, 11th International Conference. AISC, vol. 290, pp. 459–467. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07593-8_53

    Chapter  Google Scholar 

  5. Ueno, M.: Creators and Artificial Intelligence: Four-scene Comics Story Dataset with Creative Process and Metadata towards Collabolation, 4Pin1-16 (2018)

    Google Scholar 

  6. Matsui, Y., Ito, K., Aramaki, Y., Yamasaki, T., Aizawa, K.: Sketch-based manga retrieval using manga109 dataset. CoRR, Vol. abs/1510.04389 (2015)

    Google Scholar 

  7. Guérin, C., et al.: ebdtheque: a representative database of comics. In: Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR) (2013)

    Google Scholar 

  8. Cohn, N.: You’ re a good structure, Charlie Brown: the distribution of narrative categories in comic strips. Cogn. Sci. 38(7), 1317–1359 (2014)

    Google Scholar 

  9. Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971)

    Article  Google Scholar 

  10. Hinton, G.E., et al.: Improving neural networks by preventing co-adaptation of feature detectors, arXiv preprint arXiv:1207.0580 (2012)

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  12. 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, MANPU@ICPR 2016, pp. 9:1–9:6 (2016)

    Google Scholar 

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Acknowlegement

I thank to the comic artists and Spoma Inc. to corporate with this research. This work is supported by ACT-I, JST. Grant Number: JPMJPR17U4. A part of this work was supported by JSPS KAKENHI Grant, Grant-in-Aid for Scientific Research(C), 26330282.

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Correspondence to Miki Ueno .

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Ueno, M. (2019). Structure Analysis on Common Plot in Four-Scene Comic Story Dataset. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_56

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  • DOI: https://doi.org/10.1007/978-3-030-05716-9_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05715-2

  • Online ISBN: 978-3-030-05716-9

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