Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn)
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Otros documentos de la autoría: Falomir, Zoe; Museros, Lledó; Sanz, Ismael; Gonzalez Abril, Luis
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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https://doi.org/10.1016/j.eswa.2017.11.056 |
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Título
Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn)Fecha de publicación
2018-05Editor
ElsevierCita bibliográfica
FALOMIR LLANSOLA, Zoe; MUSEROS CABEDO, Lledó; SANZ BLASCO, Ismael; GONZÁLEZ ABRIL, Luis. (2018). Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn). Expert Systems with Applications, v. 97, p. 83-94Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S0957417417308126Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Expert Systems with Applications (2018), v. 9Proyecto de investigación
1) Project Cognitive Qualitative Descriptions and Applications (CogQDA) project funded bythe Universität Bremen through the 04-Independent Projects for Postdocs action; 2) Spanish Ministry of Economy and Competitiveness (HERMES,TIN2013-46801-C4-1-r); 3) Andalusian Regional Ministry of Economy, Innovation and Science (Simon, TIC-8052); 4) Spanish Ministry of Economy and Competitiveness (TIN2014-55335-R); 5) Generalitat Valenciana (GV/2015/102); 6) Universitat Jaume I (P1 · 1B2013-29)Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
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