[3M4-OS-7a] OS-7

[3M4-OS-7a-04] Visualization of Influence Relationships among Western Painters and Comparison of Features

〇Ryoko Oda¹, Eita Nakamura², Takayuki Itoh¹ (1. Ochanomizu University, 2. Kyushu University)

Keywords:Information Visualization, Western Paintings, Image gradient vector features

In recent years, researchers have actively analyzed Western paintings using information science techniques, mostly focusing on major stylistic changes rather than influence among individual painters. In this study, building on Nakamura et al.’s method, we estimated influence relationships among Western painters using multiple image features and constructed a network. We then used a previously developed visualization system to compare how different features affect network accuracy. Specifically, we employed both color features and local features (capturing small-scale color and shading variations) to estimate each painter’s “parent node” (the artist who influenced them) and compared the results with historically recognized relationships on WikiArt.org. When estimating only one parent, color features more accurately reproduced the WikiArt data; however, when estimating up to 50 parents, local features performed better. We also found that increasing the dimensionality of local features further improved accuracy. Our findings highlight how the choice and combination of features influence painter networks and may advance future image analysis methods in Western art research.