Ancient mural classification methods based on a multichannel separable network

Abstract

Abstract Ancient murals are of high artistic value and boast rich content. The accurate classification of murals is a challenging task for researchers and can be arduous even for experienced researchers. The image classification algorithms currently available are not effective in the classification of mural images with strong background noise. A new multichannel separable network model (MCSN) is proposed in this study to solve this issue. Using the GoogLeNet network model as the basic framework, we adopt a small convolution kernel for the extraction of the shallow-layer background features of murals and then decompose larger, two-dimensional convolution kernels into smaller convolution kernels, for example, 7 × 7 and 3 × 3 kernels into 7 × 1 and 1 × 7 kernels and 3 × 1 and 1 × 3 kernels, respectively, to extract important deep-layer feature information. A soft thresholding activation scaling strategy is adopted to enhance the stability of the network during training, and finally, the murals are classified through the softmax layer. A minibatch SGD algorithm is employed to update the parameters. The accuracy, recall and F1-score reached 88.16%, 90.01%, and 90.38%, respectively. Compared with mainstream classification algorithms, the model demonstrates improvement in terms of classification accuracy, generalizability, and stability to a certain extent, supporting its suitability in efficiently classifying murals.

Keywords

• Mural classification
• Multichannel separable network
• Activation scaling
• GoogLeNet