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

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 classifiers are built, and two different parameterizations have been applied for the QCD. For testing QArt-Learn approach, the Painting-91 dataset has been used, from which the paintings corresponding to Velázquez, Vermeer, Monet, Renoir, van Gogh and Gauguin were extracted, resulting in a set of 252 paintings. The results obtained have shown categorization accuracies higher than 65%, which are comparable to accuracies obtained in the literature. However, QArt-Learn uses qualitative color names which can describe style color palettes linguistically, so that they can be better understood by non-experts in art since QCDs are aligned with human perception. [ABSTRACT FROM AUTHOR]