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Art Painting Image Classification Based on Neural Network.

Xiaodong Liu1

  • 1Academy of Fine Arts, Linyi University, Linyi, Shandong 276000, China.

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Neural networks (NN) significantly enhance art painting image classification by integrating artistic style and semantic analysis. This AI approach improves accuracy, overcoming limitations of traditional methods for valuable cultural heritage.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Digital Art History

Background:

  • Traditional image classification methods struggle with art paintings due to feature loss and reliance on domain expertise.
  • Chinese art paintings hold significant cultural and historical value, necessitating effective classification methods.
  • Rapid advancements in AI, particularly neural networks and big data, offer new possibilities for art image analysis.

Purpose of the Study:

  • To investigate the efficacy of neural networks for classifying art painting images.
  • To develop a robust method for categorizing art paintings based on artistic style and semantics.
  • To create a dataset for evaluating art painting image classification performance.

Main Methods:

  • Utilized neural networks (NN) for art painting image classification.
  • Implemented an artistic style-based classification method to identify creative work styles.
  • Applied a saliency-based algorithm for semantic categorization of painting images.
  • Developed a dedicated dataset for testing and validation.

Main Results:

  • Neural network algorithms demonstrated a significant improvement in art painting image classification accuracy.
  • The combined approach of style and semantic analysis proved effective.
  • The developed dataset facilitated robust evaluation of the classification impact.

Conclusions:

  • Neural networks offer a powerful and accurate solution for classifying art painting images.
  • AI-driven classification enhances the accessibility and study of cultural heritage art.
  • This research paves the way for more sophisticated analysis of artistic works.