Construction of Image Retrieval Module for Cultural and Creative Products Based on DF-CNN

With the growth of the cultural and creative product industry, more and more cultural and creative products have been designed and published in different channels. A method based on image retrieval module is proposed to address the search problem of Chinese creative products in online channels. During the process, a cascaded forest is proposed to achieve layer by layer processing, with class vectors as the main transfer content in the entire forest system. An image attribute feature extraction process that introduces extreme gradient enhancement is designed, and the aggregation of multi-scale and multi-region features is utilized to improve image retrieval performance. The experimental results showed that in the similarity test of extracting image feature information when the image contained three composite cultural and creative objects and the total pixel amount of the image reached 7M, the similarity of image feature information was 97.6%. In the analysis of running time, the research method only took 7.4ms to generate search results in seven fields. In the analysis of the proportion of false search content, the research method maintained a false search proportion of within 6.0% when searching for a single cultural and creative product object. This indicates that the research method has higher accuracy and efficiency in image retrieval of cultural and creative products. Research methods can provide certain technical support for the development of the cultural and creative industry.

@article{Jiang2024,
  title     = {Construction of Image Retrieval Module for Cultural and Creative 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    = {Meng Jiang},
  doi       = {10.14569/IJACSA.2024.0150930},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150930}
}