title: Computerized measures of visual complexity.
abstract: Visual complexity influences people's perception of, preference for, and behaviour toward many classes of objects, from artworks to web pages. The ability to predict people's impression of the complexity of different kinds of visual stimuli holds, therefore, great potential for many domains, basic and applied. Here we use edge detection operations and several image metrics based on image compression error and Zipf's law to estimate the visual complexity of images. The experiments involved 800 images, each previously rated by thirty participants on perceived complexity. In a first set of experiments we analysed the correlation of individual features with the average human response, obtaining correlations up to rs = .771. In a second set of experiments we employed Machine Learning techniques to predict the average visual complexity score attributed by humans to each stimuli. The best configurations obtained a correlation of rs = .832. The average prediction error of the Machine Learning system over the set of all stimuli was .096 in a normalized 0 to 1 interval, showing that it is possible to predict, with high accuracy human responses. Overall, edge density and compression error were the strongest predictors of human complexity ratings.
authors.affiliation: CISUC, Department of Informatics Engineering, University of Coimbra, Portugal. Electronic address: machado@dei.uc.pt.
authors.forename: Penousal
authors.initials: P
authors.lastname: Machado
authors.affiliation: Faculty of Computer Science, University of A Coruña, Spain.
authors.forename: Juan
authors.initials: J
authors.lastname: Romero
authors.affiliation: Department of Basic Psychological Research and Research Methods, University of Vienna, Austria.
authors.forename: Marcos
authors.initials: M
authors.lastname: Nadal
authors.affiliation: Faculty of Computer Science, University of A Coruña, Spain.
authors.forename: Antonino
authors.initials: A
authors.lastname: Santos
authors.affiliation: CISUC, Department of Informatics Engineering, University of Coimbra, Portugal.
authors.forename: João
authors.initials: J
authors.lastname: Correia
authors.affiliation: Faculty of Computer Science, University of A Coruña, Spain.
authors.forename: Adrián
authors.initials: A
authors.lastname: Carballal
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D055815:Young Adult
keywords: Machine learning; Psychological aesthetics; Vision; Visual complexity