Construction of Image Retrieval Module for Ethnic Art Design Products Based on DF-CNN

With the increasing interest of consumers in ethnic art, more design products with ethnic art characteristics are being displayed. In order to help users easily retrieve related art products, an image retrieval model that can effectively extract data is proposed. The research method strengthens the depth of data mining through weighted methods, main characteristics and local features in images based on the multi-window combination, and uses the deep forest algorithm to expand the decision path and select information gain nodes. By adjusting the weights of convolutional neural networks, the retrieval ability of the model is enhanced. The gradient problem in the propagation process is optimized using residual modules, and the prominent features of the features are strengthened using a bar attention mechanism to optimize the retrieval ability. The results indicated that the loss function of the research model converged within 20 iterations, and the matching degree of the retrieved images in the testing set reached 91.28% after iterative training. The AUC of the research model was 0.876, indicating that the model had a good performance in image retrieval and classification. The retrieval accuracy of the research model was higher than other methods for image data of different specifications. This indicates that the research model has universality for multi-scale image retrieval, which can provide theoretical support for the development of ethnic art design products.

@article{He2024,
  title     = {Construction of Image Retrieval Module for Ethnic Art Design Products Based on DF-CNN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yaru He},
  doi       = {10.14569/IJACSA.2024.0150910},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150910}
}