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Article Dans Une Revue Computer Animation and Virtual Worlds Année : 2022

Deep learning for assessing the aesthetics of professional photographs

Résumé

Aesthetic quality assessment for photographs is an important research topic since it can be used for a number of applications, such as image database management or image browsing. In 2012, the aesthetic visual analysis (AVA) dataset has been proposed. It has since then been used to train the majority of computational models of aesthetics assessment. We observe that AVA is mainly composed of competitive photographs which notion of aesthetics differs from other kinds of photographs, such as professional photographs. In this paper, we evaluate whether or not recent aesthetics assessment models generalize well and perform well over professional photographs. We noticed that the different models we tested behave differently on both categories, and therefore do not generalize well. Besides, we fine-tuned one of the tested model using professional photographs and the results show that this fine-tuning is effectively improving the coverage of the methods.
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Dates et versions

hal-03900933 , version 1 (15-12-2022)

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Citer

Mathieu Chambe, Rémi Cozot, Olivier Le Meur. Deep learning for assessing the aesthetics of professional photographs. Computer Animation and Virtual Worlds, 2022, pp.14. ⟨10.1002/cav.2105⟩. ⟨hal-03900933⟩
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