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dc.contributor.authorCarballal, Adrian
dc.contributor.authorFernandez-Lozano, Carlos
dc.contributor.authorRodriguez-Fernandez, Nereida
dc.contributor.authorSantos, Iria
dc.contributor.authorRomero, Juan
dc.date.accessioned2020-06-17T14:48:45Z
dc.date.available2020-06-17T14:48:45Z
dc.date.issued2020-04-24
dc.identifier.citationCarballal, A.; Fernandez-Lozano, C.; Rodriguez-Fernandez, N.; Santos, I.; Romero, J. Comparison of Outlier-Tolerant Models for Measuring Visual Complexity. Entropy 2020, 22, 488.es_ES
dc.identifier.issn1099-4300
dc.identifier.urihttp://hdl.handle.net/2183/25732
dc.description.abstract[Abstract] Providing the visual complexity of an image in terms of impact or aesthetic preference can be of great applicability in areas such as psychology or marketing. To this end, certain areas such as Computer Vision have focused on identifying features and computational models that allow for satisfactory results. This paper studies the application of recent ML models using input images evaluated by humans and characterized by features related to visual complexity. According to the experiments carried out, it was confirmed that one of these methods, Correlation by Genetic Search (CGS), based on the search for minimum sets of features that maximize the correlation of the model with respect to the input data, predicted human ratings of image visual complexity better than any other model referenced to date in terms of correlation, RMSE or minimum number of features required by the model. In addition, the variability of these terms were studied eliminating images considered as outliers in previous studies, observing the robustness of the method when selecting the most important variables to make the prediction.es_ES
dc.description.sponsorshipInstituto de Salud Carlos III; Pl17/01826es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 2017/16es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 2017/23es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2018/49es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad and the European Regional Development Funds; UNLC08-1E-002es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; UNLC13-13-3503es_ES
dc.language.isoenges_ES
dc.publisherM D P I AGes_ES
dc.relation.urihttps://doi.org/10.3390/e22040488es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectMachine learninges_ES
dc.subjectSisual complexityes_ES
dc.subjectVisual stimulies_ES
dc.subjectCorrelationes_ES
dc.subjectHuman-computer interactiones_ES
dc.subjectCompression errores_ES
dc.subjectPsychiatry and psychologyes_ES
dc.titleComparison of Outlier-Tolerant Models for Measuring Visual Complexityes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleEntropyes_ES
UDC.volume22es_ES
UDC.startPage488es_ES
dc.identifier.doihttps://doi.org/10.3390/e22040488


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