How Panel Layouts Define Manga: Insights from Visual Ablation Experiments
This addresses the unexplored area of quantitatively analyzing visual characteristics in manga, specifically for researchers and creators interested in manga aesthetics, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of quantifying how panel layouts define manga works by training a deep learning model to predict titles from facing page images, achieving classification accuracy that demonstrates strong reflection of uniqueness in panel layouts.
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.