Data-driven cultural background fusion for environmental art image classification: Technical support of the dual Kernel squeeze and excitation network.

ABSTRACT: This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. A novel Dual Kernel Squeeze and Excitation Network (DKSE-Net) model is proposed for the complex cultural background and diverse visual representation in environmental art images. This model combines the advantages of adaptive adjustment of receptive fields using the Selective Kernel Network (SKNet) and the characteristics of enhancing channel features using the Squeeze and Excitation Network (SENet). Constructing a DKSE module can comprehensively extract the global and local features of the image. The DKSE module adopts various techniques such as dilated convolution, L2 regularization, Dropout, etc. in the multi-layer convolution process. Firstly, di

SUBMITTER: Liu C 

PROVIDER: S-EPMC11925309 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature