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MCCFNet: multi-channel color fusion network for cognitive classification of traditional Chinese paintings.

Geng, Jing; Zhang, Xin; Yan, Yijun; Sun, Meijun; Zhang, Huiyuan; Assaad, Maher; Ren, Jinchang; Li, Xiaoquan

Authors

Jing Geng

Xin Zhang

Meijun Sun

Huiyuan Zhang

Maher Assaad

Xiaoquan Li



Abstract

The computational modeling and analysis of traditional Chinese painting rely heavily on cognitive classification based on visual perception. This approach is crucial for understanding and identifying artworks created by different artists. However, the effective integration of visual perception into artificial intelligence (AI) models remains largely unexplored. Additionally, the classification research of Chinese painting faces certain challenges, such as insufficient investigation into the specific characteristics of painting images for author classification and recognition. To address these issues, we propose a novel framework called multi-channel color fusion network (MCCFNet), which aims to extract visual features from diverse color perspectives. By considering multiple color channels, MCCFNet enhances the ability of AI models to capture intricate details and nuances present in Chinese painting. To improve the performance of the DenseNet model, we introduce a regional weighted pooling (RWP) strategy specifically designed for the DenseNet169 architecture. This strategy enhances the extraction of highly discriminative features. In our experimental evaluation, we comprehensively compared the performance of our proposed MCCFNet model against six state-of-the-art models. The comparison was conducted on a dataset consisting of 2436 TCP samples, derived from the works of 10 renowned Chinese artists. The evaluation metrics employed for performance assessment were Top-1 Accuracy and the area under the curve (AUC). The experimental results have shown that our proposed MCCFNet model significantly outperform all other benchmarking methods with the highest classification accuracy of 98.68%. Meanwhile, the classification accuracy of any deep learning models on TCP can be much improved when adopting our proposed framework.

Citation

GENG, J., ZHANG, X., YAN, Y., SUN, M., ZHANG, H., ASSAAD, M., REN, J. and LI, X. 2023. MCCFNet: multi-channel color fusion network for cognitive classification of traditional Chinese paintings. Cognitive computation [online],15(6), pages 2050-2061. Available from: https://doi.org/10.1007/s12559-023-10172-1

Journal Article Type Article
Acceptance Date Jun 27, 2023
Online Publication Date Jul 18, 2023
Publication Date Nov 30, 2023
Deposit Date Jul 27, 2023
Publicly Available Date Jul 27, 2023
Journal Cognitive computation
Print ISSN 1866-9956
Electronic ISSN 1866-9964
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 15
Issue 6
Pages 2050-2061
DOI https://doi.org/10.1007/s12559-023-10172-1
Keywords Visual cognition; Multi-channel color fusion network (MCCFNet); Regional weighted pooling (RWP); Chinese painting classification
Public URL https://rgu-repository.worktribe.com/output/2002185

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