Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn)

@article{Falomir2018CategorizingPI,
  title={Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn)},
  author={Zoe Falomir and Lled{\'o} Museros Cabedo and Ismael Sanz and Luis Gonz{\'a}lez Abril},
  journal={Expert Syst. Appl.},
  year={2018},
  volume={97},
  pages={83-94}
}
Abstract The QArt-Learn approach for style painting categorization based on Qualitative Color Descriptors (QCD), color similarity (SimQCD), and quantitative global features (i.e. average of brightness, hue, saturation and lightness and brightness contrast) is presented in this paper. k-Nearest Neighbor (k-NN) and support vector machine (SVM) techniques have been used for learning the features of paintings from the Baroque, Impressionism and Post-Impressionism styles. Specifically two… CONTINUE READING
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  • The results obtained have shown categorization accuracies higher than 65%, which are comparable to accuracies obtained in the literature.

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