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. 2022 Jul 4:2022:3119604.
doi: 10.1155/2022/3119604. eCollection 2022.

Art Painting Image Classification Based on Neural Network

Xiaodong Liu  1
Affiliations

Art Painting Image Classification Based on Neural Network

Xiaodong Liu. Comput Intell Neurosci. .

Retraction in

Abstract

Neural network (NN) is among the most important and vital form of artificial intelligence which are utilized for the classification of data, information, or images. Moreover, NN has been extensively utilized in various research domains throughout the world, and it is because of overwhelming properties. Painting is a form formed by China's long history and culture, and a large number of paintings reflect the living conditions of China in different periods, which is of great value to the development of China's culture. Image classification has become a key research content in the field of image in the stage of rapid development of information technology, and the content of art painting image classification has also developed rapidly. At present, most traditional image classification methods are formed on the basis of shallow structure learning algorithm, and there are many types of image features that can be extracted, but some features will be lost when extracting, and we need to master the basic painting knowledge. As a result, this extraction process is not general, which explains why traditional Chinese art picture classification is not ubiquitous. The fast development of big data technology and neural network algorithms in recent years has the potential to speed up the categorization of art painting images. As a result, this research investigates the use of neural networks to classify art painting images. The painting image classification method based on artistic style is used to determine the styles of distinct creative works, and the painting image classification algorithm based on saliency is then used to categorize the picture semantics. Finally, a dataset for testing the categorization impact of art painting pictures is developed. The results show that the neural network algorithm can significantly improve the classification effect of art painting images with higher accuracy.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Structure diagram of artificial neural network.
Figure 2
Figure 2
Image recognition process.
Figure 3
Figure 3
Image classification algorithm flow based on convolution neural network.
Figure 4
Figure 4
Accuracy analysis of Dunhuang mural dataset.
Figure 5
Figure 5
Oriental painting image training classification accuracy.
Figure 6
Figure 6
Accuracy of Western painting image classification.
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