A Multi-Branch Residual Network Based on Depth Correlation Features for the Classification of Chinese Ink Paintings.

Despite significant strides in digital classification of Chinese ink paintings, existing methods predominantly rely on low-level features, insufficient for capturing the nuanced artistic styles of such works. This study introduces a novel multi-branch residual network that leverages depth correlation features to enhance the classification of Chinese ink paintings. We innovatively combine global style features, extracted using the Gram matrix, with local brushstroke features obtained via the Holistically-Nested Edge Detection(HED) method. This dual-feature approach addresses the limitations of previous studies by incorporating high-level stylistic nuances alongside low-level details, resulting in a more robust classification system. Quantitative results demonstrate a marked improvement in classification accuracy, with our network outperforming existing state-of-the-art models by significant margins in both artist and genre classification tasks. This advancement not only underscores the efficacy of integrating diverse feature sets but also paves the way for more sensitive and accurate management of digital art repositories. [ABSTRACT FROM AUTHOR]