An Application of Neural Embedding Models for Representing Artistic Periods

Title

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

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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Springer

An Application of Neural Embedding Models for Representing Artistic Periods

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