An Application of Neural Embedding Models for Representing Artistic Periods
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Description
We showcase visualizations created for art periods of Dalí, van Gogh, and Picasso by leveraging deep neural embedding models like word2vec to represent color features. First, the embedding vectors are generated for every color used in artworks of these painters. Next, t-distributed Stochastic Neighbor Embedding (t-SNE) is applied to generate a two-dimensional visualizations of the color space. Colors used in close proximity on the canvas are observed as compact clusters in the visualizations. These visualizations are termed as fingerprints, as they uniquely depict each art period of a painter, by highlighting the color palette used in their works. The authors further provide commentary on the artists’ art periods and how the fingerprints showcase their artistic evolution.
ISBN
978-3-031-03789-4
Publication Date
4-15-2022
Publisher
Springer
City
Cham, Switzerland
Keywords
Art periods, Neural embeddings, Word embeddings, t-SNE
Disciplines
Art and Design | Data Science | Data Storage Systems | Other Computer Sciences | Painting
Recommended Citation
Ali, R.H., Rhodeghiero, K., Zuch, A., Syed, S., Linstead, E. (2022). An Application of Neural Embedding Models for Representing Artistic Periods. In: Martins, T., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2022. Lecture Notes in Computer Science, vol 13221. Springer, Cham. https://doi.org/10.1007/978-3-031-03789-4_21
Copyright
Springer
Comments
In Tiago Martins,Nereida Rodríguez-Fernández, and Sérgio M. Rebelo (Eds.), Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2022. Lecture Notes in Computer Science, vol 13221. https://doi.org/10.1007/978-3-031-03789-4_21
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