An art painting style explainable classifier grounded on logical and commonsense reasoning
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Otros documentos de la autoría: Costa, Vicent; Alonso Moral, Jose Maria; Falomir, Zoe; Dellunde, Maria Pilar
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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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INVESTIGACIONMetadatos
Título
An art painting style explainable classifier grounded on logical and commonsense reasoningFecha de publicación
2023-05-17Editor
SpringerISSN
1432-7643; 1433-7479Cita bibliográfica
Costa, V., Alonso-Moral, J.M., Falomir, Z. et al. An art painting style explainable classifier grounded on logical and commonsense reasoning. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08258-xTipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper presents the art painting style explainable classifier named ANYXI. The classifier is based on art specialists’ knowledge of art styles and human-understandable color traits. ANYXI overcomes the principal ... [+]
This paper presents the art painting style explainable classifier named ANYXI. The classifier is based on art specialists’ knowledge of art styles and human-understandable color traits. ANYXI overcomes the principal flaws in the few art painting style classifiers in the literature. In this way, we first propose, using the art specialists’ studies, categorizations of the Baroque, Impressionism, and Post-Impressionism. Second, we carry out a human survey with the aim of validating the appropriateness of the color features used in the categorizations for human understanding. Then, we analyze and discuss the accuracy and interpretability of the ANYXI classifier. The study ends with an evaluation of the rationality of explanations automatically generated by ANYXI. We enrich the discussion and empirical validation of ANYXI by considering a quantitative and qualitative comparison versus other explainable classifiers. The reported results show how ANYXI is outstanding from the point of view of interpretability while keeping high accuracy (comparable to non-explainable classifiers). Moreover, automated generations are endowed with a good level of rationality. [-]
Entidad financiadora
MCIN/AEI/10.13039/501100011033 | Galician Ministry of Culture, Education, Professional Training and University | European Union and FEDER/ERDF (European Regional Development Funds) | H2020-MSCARISE- 2020 project MOSAIC | Ramon-y-Cajal fellowship | Ministerio de Ciencia, Innovación y Universidades
Código del proyecto o subvención
PID2021-123152OB-C21 | PID2021-123152OB-C22 | RED2022-134315-T | ED431G2019/04 | ED431C2022/19 | 101007627 | RYC2019-027177-I / AEI / 10.13039/501100011033 | FJC2021-047274-I
Derechos de acceso
© The Author(s) 2023
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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