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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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How to Represent Paintings: A Painting Classification Using Artistic Comments.

Wentao Zhao1,2, Dalin Zhou3, Xinguo Qiu1

  • 1College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

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Summary
This summary is machine-generated.

This study introduces a novel Art Graph Convolutional Network (ArtGCN) for analyzing paintings using artistic comments and natural language processing (NLP). The method effectively classifies art types, schools, timeframes, and authors, achieving state-of-the-art results.

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art classificationgraph convolutional networksmachine learningnatural language processing

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Machine Learning
  • Art History

Background:

  • Traditional automatic painting analysis relies on computer vision for image representation.
  • Existing methods often focus on visual features like color, limiting nuanced understanding.
  • There is a need for advanced techniques to capture deeper semantic content in artworks.

Purpose of the Study:

  • To develop a novel method for classifying paintings using artistic comments.
  • To leverage graph convolutional networks and natural language processing for art analysis.
  • To improve the classification of painting type, school, timeframe, and author.

Main Methods:

  • Constructed a single artistic comment graph based on word co-occurrence and document-word relations.
  • Implemented an Art Graph Convolutional Network (ArtGCN) trained on the entire corpus.
  • Utilized one-hot representation for graph nodes (words and documents) and learned joint embeddings supervised by known painting labels.

Main Results:

  • The proposed ArtGCN method achieved state-of-the-art performance across various classification tasks.
  • Demonstrated the effectiveness of using artistic comments over visual features for classification.
  • Learned effective word and painting embeddings crucial for label description and image retrieval.

Conclusions:

  • ArtGCN offers a powerful approach for large-scale automatic painting analysis.
  • Natural language processing and graph convolutional networks can significantly enhance art classification and retrieval.
  • The learned embeddings provide valuable insights into art descriptions and facilitate content-based image retrieval.