Automatic detection and visualization of garment color in Western portrait paintings

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Abstract

Paintings give us important clues about how males and females were perceived over centuries in the Western culture. In this article, we describe a system that allows scholars to automatically visualize how the clothing colors of male and female subjects changed over time. Our system analyzes a large database of paintings, locates portraits, automatically classifies each portrait's subject as either male or female, segments the clothing areas and finds their dominant color. An interactive, web-based visualization is proposed to allow further exploration of the results. To test the accuracy of our system, we manually annotate a portion of the Rijksmuseum collection, and use state-of-the-art image processing and computer vision algorithms to process the paintings. We use a deep neural network-based style transfer approach to improve gender recognition (or more correctly, sex recognition) of the sitters of portraits. The annotations and the code of the approach are made available.

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Sarl, C., Salah, A. A., & Akdag Salah, A. A. (2019). Automatic detection and visualization of garment color in Western portrait paintings. Digital Scholarship in the Humanities, 34, I156–I171. https://doi.org/10.1093/llc/fqz055

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