How Convolutional Neural Networks Remember Art (CROSBI ID 663957)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Cetinić, Eva ; Lipić, Tomislav ; Grgić, Sonja
engleski
How Convolutional Neural Networks Remember Art
Inspired by the successful performance of Convolutional Neural Networks (CNN) in automatically predicting complex image properties such as memorability, in this work we explore their transferability to the domain of art images. We employ a CNN model trained to predict memorability scores of natural images to explore the memorability of artworks belonging to different genres and styles. Our experiments show that nude painting and portrait are the most memorable genres, while landscape and marine painting are the least memorable. Regarding image style, we show that abstract styles tend to be more memorable than figurative. Additionally, on the subset of abstract images, we explore the relation between memorability and other features related to composition and color, as well as the sentiment evoked by the image. We show that there is no correlation between symmetry and memorability, however memorability positively correlates with the image’s probability of evoking positive sentiment.
Image Memorability ; Fine Art ; Convolutional Neural Networks
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Podaci o prilogu
67
2018.
objavljeno
10.1109/IWSSIP.2018.8439497
Podaci o matičnoj publikaciji
Proceedings of the International Conference on Systems, Signals and Image Processing - IWSSIP 2018
Planinšič, Peter ; Gleich, Dušan
Piscataway (NJ): Institute of Electrical and Electronics Engineers (IEEE)
978-1-5386-6979-2
2157-8702
Podaci o skupu
25th International Conference on Systems, Signals and Image Processing (IWSSIP 2018)
predavanje
20.06.2018-22.06.2018
Maribor, Slovenija