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Toward automated discovery of artistic influence

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Abstract

Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question “Who influenced this artist?” by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works

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Authors and Affiliations

  1. Department of Computer Science, Rutgers, The State University of New Jersey, 110 Frelinghuysen Road, Piscataway, NJ, 08854-8019, USA

    Babak Saleh, Kanako Abe, Ravneet Singh Arora & Ahmed Elgammal

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  1. Babak Saleh
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  2. Kanako Abe
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  3. Ravneet Singh Arora
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  4. Ahmed Elgammal
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Correspondence to Babak Saleh.

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Saleh, B., Abe, K., Arora, R.S. et al. Toward automated discovery of artistic influence. Multimed Tools Appl 75, 3565–3591 (2016). https://doi.org/10.1007/s11042-014-2193-x

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  • DOI: https://doi.org/10.1007/s11042-014-2193-x

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