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dc.contributor.authorCosta, Vicent
dc.contributor.authorAlonso Moral, Jose Maria
dc.contributor.authorFalomir, Zoe
dc.contributor.authorDellunde, Maria Pilar
dc.date.accessioned2023-07-24T08:12:26Z
dc.date.available2023-07-24T08:12:26Z
dc.date.issued2023-05-17
dc.identifier.citationCosta, 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-xca_CA
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.urihttp://hdl.handle.net/10234/203524
dc.description.abstractThis 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.ca_CA
dc.format.extent23 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.relation.uriThe QArt-337 dataset, images, scripts needed for reproducibility of experiments as well as logs with reported results are available at https://gitlab.citius.usc.es/jose.alonso/xai4art/.ca_CA
dc.rights© The Author(s) 2023ca_CA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/ca_CA
dc.subjectknowledge representation and reasoningca_CA
dc.subjectexplainable artificial intelligenceca_CA
dc.subjectfuzzy logicca_CA
dc.subjecthuman-centered artificial intelligenceca_CA
dc.titleAn art painting style explainable classifier grounded on logical and commonsense reasoningca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s00500-023-08258-x
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameMCIN/AEI/10.13039/501100011033ca_CA
project.funder.nameGalician Ministry of Culture, Education, Professional Training and Universityca_CA
project.funder.nameEuropean Union and FEDER/ERDF (European Regional Development Funds)ca_CA
project.funder.nameH2020-MSCARISE- 2020 project MOSAICca_CA
project.funder.nameRamon-y-Cajal fellowshipca_CA
project.funder.nameMinisterio de Ciencia, Innovación y Universidadesca_CA
oaire.awardNumberPID2021-123152OB-C21ca_CA
oaire.awardNumberPID2021-123152OB-C22ca_CA
oaire.awardNumberRED2022-134315-Tca_CA
oaire.awardNumberED431G2019/04ca_CA
oaire.awardNumberED431C2022/19ca_CA
oaire.awardNumber101007627ca_CA
oaire.awardNumberRYC2019-027177-I / AEI / 10.13039/501100011033ca_CA
oaire.awardNumberFJC2021-047274-Ica_CA


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© The Author(s) 2023
Except where otherwise noted, this item's license is described as © The Author(s) 2023