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Toward automated discovery of artistic influence

Published: 01 April 2016 Publication History

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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Cited By

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  • (2023)Identifying Relationships and Classifying Western-style Paintings: Machine Learning Approaches for Artworks by Western Artists and Meiji-era Japanese ArtistsJournal on Computing and Cultural Heritage 10.1145/363113617:1(1-18)Online publication date: 13-Nov-2023
  • (2023)Collaborative Creativity in TikTok Music DuetsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581380(1-16)Online publication date: 19-Apr-2023
  • (2023)Identifying influences between artists based on artwork faces and geographic proximityComputers and Graphics10.1016/j.cag.2023.05.028114:C(116-125)Online publication date: 1-Aug-2023
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Information & Contributors

Information

Published In

cover image Multimedia Tools and Applications
Multimedia Tools and Applications  Volume 75, Issue 7
April 2016
577 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2016

Author Tags

  1. Automated artistic-influence discovery
  2. Content-based image retrieval
  3. Digital humanity
  4. Image similarity
  5. Knowledge discovery
  6. Painting style classification
  7. Unsupervised learning

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Cited By

View all
  • (2023)Identifying Relationships and Classifying Western-style Paintings: Machine Learning Approaches for Artworks by Western Artists and Meiji-era Japanese ArtistsJournal on Computing and Cultural Heritage 10.1145/363113617:1(1-18)Online publication date: 13-Nov-2023
  • (2023)Collaborative Creativity in TikTok Music DuetsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581380(1-16)Online publication date: 19-Apr-2023
  • (2023)Identifying influences between artists based on artwork faces and geographic proximityComputers and Graphics10.1016/j.cag.2023.05.028114:C(116-125)Online publication date: 1-Aug-2023
  • (2022)A Deep Learning Approach to Clustering Visual ArtsInternational Journal of Computer Vision10.1007/s11263-022-01664-y130:11(2590-2605)Online publication date: 1-Nov-2022
  • (2021)Studying Three Abstract Artists Based on a Multiplex Network Knowledge RepresentationComplexity10.1155/2021/85065712021Online publication date: 1-Jan-2021
  • (2021)Neural NetworkSecurity and Communication Networks10.1155/2021/10663382021Online publication date: 1-Jan-2021
  • (2021)Exploring the Representativity of Art PaintingsIEEE Transactions on Multimedia10.1109/TMM.2020.301688723(2794-2805)Online publication date: 1-Jan-2021
  • (2021)Visual link retrieval and knowledge discovery in painting datasetsMultimedia Tools and Applications10.1007/s11042-020-09995-z80:5(6599-6616)Online publication date: 1-Feb-2021
  • (2021)Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overviewNeural Computing and Applications10.1007/s00521-021-05893-z33:19(12263-12282)Online publication date: 1-Oct-2021
  • (2021)A Brief Overview of Deep Learning Approaches to Pattern Extraction and Recognition in Paintings and DrawingsPattern Recognition. ICPR International Workshops and Challenges10.1007/978-3-030-68796-0_35(487-501)Online publication date: 10-Jan-2021
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