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dc.contributor.authorFernández-Lozano, Carlos
dc.contributor.authorCarballal, Adrian
dc.contributor.authorMachado, Penousal
dc.contributor.authorSantos, Antonino
dc.contributor.authorRomero, Juan
dc.date.accessioned2019-09-26T13:45:21Z
dc.date.available2019-09-26T13:45:21Z
dc.date.issued2019-07-18
dc.identifier.citationFernandez-Lozano C, Carballal A, Machado P, Santos A, Romero J. 2019. Visual complexity modelling based on image features fusion of multiple kernels. PeerJ 7:e7075 https://doi.org/10.7717/peerj.7075es_ES
dc.identifier.issn2167-8359
dc.identifier.urihttp://hdl.handle.net/2183/23991
dc.description.abstract[Abstract] Humans’ perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf’s law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans’ perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.es_ES
dc.description.sponsorshipXunta de Galicia; GRC2014/049es_ES
dc.description.sponsorshipPortuguese Foundation for Science and Technology; SBIRC; PTDC/EIA EIA/115667/2009es_ES
dc.description.sponsorshipXunta de Galicia; Ref. XUGA-PGIDIT-10TIC105008-PRes_ES
dc.description.sponsorshipMinisterio de Ciencia y Tecnología; TIN2008-06562/TINes_ES
dc.description.sponsorshipMinisterio de Ecnomía y Competitividad; FJCI-2015-26071es_ES
dc.language.isoenges_ES
dc.publisherPeerJ, Ltd.es_ES
dc.relation.urihttps://doi.org/10.7717/peerj.7075es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectPsychiatry and Psychologyes_ES
dc.subjectHuman–Computer Interactiones_ES
dc.subjectComputational Sciencees_ES
dc.subjectData Mining and Machine Learninges_ES
dc.subjectCorrelationes_ES
dc.subjectMachine learninges_ES
dc.subjectZipf’s lawes_ES
dc.subjectCompression errores_ES
dc.subjectVisual stimulies_ES
dc.subjectVisual complexityes_ES
dc.titleVisual complexity modelling based on image features fusion of multiple kernelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitlePeerJes_ES
dc.identifier.doi10.7717/peerj.7075


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