Towards automated classification of fine-art painting style

Arora, Ravneet; Elgammal, Ahmed

doi:10.7282/t3xp73qp

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2023

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Cited by 6 publications

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“…Collaborations between computer science and art history have led to the production of similar machine learning technologies for visual media such as classifying painting genres (Arora and Elgammal, 2012;Elgammal, et al, 2018) and identifying undiscovered works of artists (Cömert, et al, 2021).…”

Section: Publication Anonymization and Privacymentioning

confidence: 99%

Ethics and images in social media research

Taylor¹,

Valencia-García²,

VandenBroek³

et al. 2023

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This study examines strategies for anonymizing online data in a way that follows ethical guidelines while also retaining the fluid nature of participant engagement with social media. We explore unanticipated ethical issues that emerged as we began capturing and anonymizing social media posts for our ethnography of college students and online sociality. Using examples from our data, we illustrate our penciling technique to anonymize images for the naked eye while also considering the digital footprint. We consider methodological and ethical challenges of anonymization in a specific internet research context — user generated social media images obtained with informed consent — and explore the implications of our findings on the potential life of digital content.

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Section: Publication Anonymization and Privacymentioning

confidence: 99%