Abstract
A technique called neural style transfer is an effective method for generating artistic images based on a deep learning technique. It can extract a mood of a specific painting and blends it with a different image. The original method, however, needs a high-performance computer to get an output image within a practical response time since the neural style transfer involves heavily-loaded processing. To solve the problem, we develop a web-based image editing system enabling users to readily access the function only by using a mobile device with a standard web browser and a network connection. The proposed system allows the users to easily generating a wide variety of artistic images like logos and image clips using the neural style transfer anywhere they have a connection to the Internet. We implement the system as a web application and conduct some experiments to verify the effectiveness of the system. We elaborate the implementation method, experimental results, and observations in this paper.
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References
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, pp. 1–14 (2015)
Champandard, A.J.: Semantic style transfer and turning two-bit doodles into fine artworks. arXiv:1603.01768, pp. 1–7 (2016)
Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6997–7005 (2016)
Goto, K., Nishino, H.: A method of neural style transfer for images with artistic characters. In: Proceedings of the 12th International Conference on Complex, Intelligent and Software Intensive Systems, pp. 911–920 (2018)
The MariaDB Foundation. https://mariadb.org/. Accessed Jan 2019
Miura, S., Nishino, H.: A practical colour scheme explorer for designers. Int. J. Space-Based Situated Comput. 7(3), 155–165 (2017)
Bianco, S., Celona, L., Napoletano, P., Schettini, R.: Predicting image aesthetics with deep learning. In: Proceedings of the 17th International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 117–125 (2016)
Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.: Rating image aesthetics using deep learning. IEEE Trans. Multimedia 17(11), 2021–2034 (2015)
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Goto, K., Nishino, H. (2020). A Web-Based Artwork Editing System Empowered by Neural Style Transfer. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_49
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DOI: https://doi.org/10.1007/978-3-030-15032-7_49
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