| Title |
The Application of Dual Path Network Painting Stroke Feature Extraction in Art Image Classification |
| Authors |
(Jin Ma) ; (Wei Sun) ; (Yu Zhang) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.4.495 |
| Keywords |
Dual path network; Stroke information; Gray level co-occurrence matrix; Support vector machine; Art images |
| Abstract |
To solve the traditional image classification issues focusing on local info while neglecting overall info and forgetting stroke info, and enhance natural image classification models, this study suggests a dual-channel, dual-path art image classification model. By setting up three primary colors and stroke information channels, combined with dual path networks and support vector machines, further extracting image feature information for classification.
The experiment showcases that the dual channel constructed by the research institute has a minimum error rate of 42.13% for the Top-1 indicator in feature information extraction, which is 9.25% higher than the commonly used single channel Gram model in error rate accuracy. The error rate of the Top-1 index for art images of different styles, genres, and authors in the dual path network model is 11.73%, while the error rate of the Top-5 index is 0.73%. The dual channel dual path network model has a recognition and classification accuracy of 91.56% for different styles of art images, which is at least 1.25% higher than the commonly used image classification models. The experiment indicates indicate that the dual channel dual path network model constructed by the research institute has good classification performance. |