Adaptive classification of artistic images using multi-scale convolutional neural networks.

ABSTRACT: The current art image classification methods have low recall and accuracy rate issues . To improve the classification performance of art images, a new adaptive classification method is designed employing multi-scale convolutional neural networks (CNNs). Firstly, the multi-scale Retinex algorithm with color recovery is used to complete the enhancement processing of art images. Then the extreme pixel ratio is utilized to evaluate the image quality and obtain the art image that can be analyzed. Afterward, edge detection technology is implemented to extract the key features in the image and use them as initial values of the item to be trained in the classification model. Finally, a multi-scale convolutional neural network (CNN) is constructed by using extended convolutions, and the characteris

SUBMITTER: Xiang J 

PROVIDER: S-EPMC11622833 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature