Formation and Schema Analysis of Oil Painting Style Based on Texture and Color Texture Features under Few Shot

Area of Science:

• Art History
• Computer Vision
• Digital Art

Background:

• Texture and color are crucial for conveying artistic meaning in oil paintings.
• Limited oil painting datasets hinder the study of these features and the development of modern styles.

Purpose of the Study:

• To investigate texture and color characteristics in oil paintings using a small sample size.
• To develop a model for extracting artistic features to promote modern oil painting development.

Main Methods:

• A deep neural network model was established to extract image features and color textures.
• The model was trained and tested on a limited dataset of oil painting artworks.

Main Results:

• The deep neural network achieved over 85% accuracy in extracting texture and color features from small sample oil painting images.
• The model demonstrated high definition in feature extraction.

Conclusions:

• Deep neural networks are effective for analyzing oil painting art, even with limited data.
• This approach provides significant guidance for the research and creation of modern oil painting art.