Abstract artists use non-figurative elements (i.e. colours, lines, shapes, and textures) to convey emotions and often rely on the titles of their various compositions to generate (or enhance) an emotional reaction in the audience. Several psychological works observed that the metadata (i.e., titles, description and/or artist statements) associated with paintings increase the understanding and the aesthetic appreciation of artworks. In this paper we explore if the same metadata could facilitate the computational analysis of artworks, and reveal what kind of emotional responses they awake. To this end, we employ computer vision and sentiment analysis to learn statistical patterns associated with positive and negative emotions on abstract paintings. We propose a multimodal approach which combines both visual and metadata features in order to improve the machine performance. In particular, we propose a novel joint flexible Schatten p-norm model which can exploit the sharing patterns between visual and textual information for abstract painting emotion analysis. Moreover, we conduct a qualitative analysis on the cases in which metadata help improving the machine performance.

Looking at Mondrian's victory Boogie-Woogie: what do i feel? / Sartori, Andreza; Yan, Yan; Ozbal, Gozde; Salah, A. A. Akdag; Salah, A.; Sebe, Niculae. - (2015), pp. 2503-2509. ((Intervento presentato al convegno IJCAI 2015 tenutosi a Buenos Aires nel 25-31 July, 2015.

Looking at Mondrian's victory Boogie-Woogie: what do i feel?

Sartori, Andreza;Yan, Yan;Ozbal, Gozde;Sebe, Niculae
2015

Abstract

Abstract artists use non-figurative elements (i.e. colours, lines, shapes, and textures) to convey emotions and often rely on the titles of their various compositions to generate (or enhance) an emotional reaction in the audience. Several psychological works observed that the metadata (i.e., titles, description and/or artist statements) associated with paintings increase the understanding and the aesthetic appreciation of artworks. In this paper we explore if the same metadata could facilitate the computational analysis of artworks, and reveal what kind of emotional responses they awake. To this end, we employ computer vision and sentiment analysis to learn statistical patterns associated with positive and negative emotions on abstract paintings. We propose a multimodal approach which combines both visual and metadata features in order to improve the machine performance. In particular, we propose a novel joint flexible Schatten p-norm model which can exploit the sharing patterns between visual and textual information for abstract painting emotion analysis. Moreover, we conduct a qualitative analysis on the cases in which metadata help improving the machine performance.
Proceedings of the 24th International Joint Conference on Artificial Intelligence
Palo Alto>>, CA
AAAI Press
978-1-57735-738-4
Sartori, Andreza; Yan, Yan; Ozbal, Gozde; Salah, A. A. Akdag; Salah, A.; Sebe, Niculae
Looking at Mondrian's victory Boogie-Woogie: what do i feel? / Sartori, Andreza; Yan, Yan; Ozbal, Gozde; Salah, A. A. Akdag; Salah, A.; Sebe, Niculae. - (2015), pp. 2503-2509. ((Intervento presentato al convegno IJCAI 2015 tenutosi a Buenos Aires nel 25-31 July, 2015.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/115063
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