Study of automatic generation of motif tag in Nishiki-e

Hoshito Minagi; Takuzi Suzuki; Yoshitugu Manabe; Noriko Yata

doi:10.1117/12.2626097

30 April 2022 Study of automatic generation of motif tag in Nishiki-e

Hoshito Minagi, Takuzi Suzuki, Yoshitugu Manabe, Noriko Yata

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 1217717 (2022) https://doi.org/10.1117/12.2626097
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

The motif drawn on Nishiki-e is needed to register in the database as a search tag. The accuracies of the motif tag that are currently manually registered is unstable because it depends on the knowledge and interests of the registrant. Therefore, this study proposes an automatic generation method of motif tags using deep learning to support cultural activities. Nishiki-e is more difficult to collect training images that include specific motifs than photographs. In this study, we propose three methods for preparing training images. First, we applied a similar image generation model from a single image to a small number of Nishiki-e containing motifs to create training images. Second, we applied a Nishiki-e style processing model to photographs containing motifs to create training images. Third, we combined a small number of photographs with motifs with some background images to create training images. In particular, the third method can detect from a small number of inputs like the first method with an accuracy close to that of the second method.

Citation Download Citation

Hoshito Minagi, Takuzi Suzuki, Yoshitugu Manabe, and Noriko Yata "Study of automatic generation of motif tag in Nishiki-e", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 1217717 (30 April 2022); https://doi.org/10.1117/12.2626097

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KEYWORDS

Image segmentation

Photography

Image processing

Composites

Databases

Image fusion

Image registration

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