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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2412.19141 |
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| _version_ | 1866916743491878912 |
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| author | Feng, Siyuan Yoshinaga, Teruya Hayashi, Katsuhiko Washio, Koki Kamigaito, Hidetaka |
| author_facet | Feng, Siyuan Yoshinaga, Teruya Hayashi, Katsuhiko Washio, Koki Kamigaito, Hidetaka |
| contents | Today, manga has gained worldwide popularity. However, the question of how various elements of manga, such as characters, text, and panel layouts, reflect the uniqueness of a particular work, or even define it, remains an unexplored area. In this paper, we aim to quantitatively and qualitatively analyze the visual characteristics of manga works, with a particular focus on panel layout features. As a research method, we used facing page images of manga as input to train a deep learning model for predicting manga titles, examining classification accuracy to quantitatively analyze these features. Specifically, we conducted ablation studies by limiting page image information to panel frames to analyze the characteristics of panel layouts. Through a series of quantitative experiments using all 104 works, 12 genres, and 10,122 facing page images from the Manga109 dataset, as well as qualitative analysis using Grad-CAM, our study demonstrates that the uniqueness of manga works is strongly reflected in their panel layouts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19141 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Panel Layouts Define Manga: Insights from Visual Ablation Experiments Feng, Siyuan Yoshinaga, Teruya Hayashi, Katsuhiko Washio, Koki Kamigaito, Hidetaka Computer Vision and Pattern Recognition Today, manga has gained worldwide popularity. However, the question of how various elements of manga, such as characters, text, and panel layouts, reflect the uniqueness of a particular work, or even define it, remains an unexplored area. In this paper, we aim to quantitatively and qualitatively analyze the visual characteristics of manga works, with a particular focus on panel layout features. As a research method, we used facing page images of manga as input to train a deep learning model for predicting manga titles, examining classification accuracy to quantitatively analyze these features. Specifically, we conducted ablation studies by limiting page image information to panel frames to analyze the characteristics of panel layouts. Through a series of quantitative experiments using all 104 works, 12 genres, and 10,122 facing page images from the Manga109 dataset, as well as qualitative analysis using Grad-CAM, our study demonstrates that the uniqueness of manga works is strongly reflected in their panel layouts. |
| title | How Panel Layouts Define Manga: Insights from Visual Ablation Experiments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.19141 |