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Comparison of Outlier-Tolerant Models for Measuring Visual Complexity
dc.contributor.author | Carballal, Adrián | |
dc.contributor.author | Fernández-Lozano, Carlos | |
dc.contributor.author | Rodríguez-Fernández, Nereida | |
dc.contributor.author | Santos, Iria | |
dc.contributor.author | Romero, Juan | |
dc.date.accessioned | 2020-06-17T14:48:45Z | |
dc.date.available | 2020-06-17T14:48:45Z | |
dc.date.issued | 2020-04-24 | |
dc.identifier.citation | Carballal, A.; Fernandez-Lozano, C.; Rodriguez-Fernandez, N.; Santos, I.; Romero, J. Comparison of Outlier-Tolerant Models for Measuring Visual Complexity. Entropy 2020, 22, 488. https://doi.org/10.3390/e22040488 | es_ES |
dc.identifier.issn | 1099-4300 | |
dc.identifier.uri | http://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.sponsorship | The Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER) “A way to build Europe” support this work through the “Colaborative Project in Genomic Data Integration (CICLOGEN)” Pl17/01826. This work has also been supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). On the other hand, the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) was funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Funds (FEDER) | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/16 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/23 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PI17%2F01826/ES/Proyecto Colaborativo de Integración de datos Genómicos (CICLOGEN). Técnicas de data mining y docking molecular para análisis de datos integrativos en cáncer de colon/ | |
dc.relation | info:eu-repo/grantAgreement/MEC/Plan Nacional de I+D+i 2008-2011/UNLC08-1E-002/ES/Infraestructura computacional para la Red Gallega de Bioinformática/ | |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/UNLC13-1E-2503/ES/Plataforma HPC-PLUS para aplicaciones biomédicas/ | |
dc.relation.uri | https://doi.org/10.3390/e22040488 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Machine learning | es_ES |
dc.subject | Sisual complexity | es_ES |
dc.subject | Visual stimuli | es_ES |
dc.subject | Correlation | es_ES |
dc.subject | Human-computer interaction | es_ES |
dc.subject | Compression error | es_ES |
dc.subject | Psychiatry and psychology | es_ES |
dc.title | Comparison of Outlier-Tolerant Models for Measuring Visual Complexity | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Entropy | es_ES |
UDC.volume | 22 | es_ES |
UDC.startPage | 488 | es_ES |
dc.identifier.doi | 10.3390/e22040488 |
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