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Towards a Tool for Visual Link Retrieval and Knowledge Discovery in Painting Datasets

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Digital Libraries: The Era of Big Data and Data Science (IRCDL 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1177))

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Abstract

This paper presents a preliminary investigation aimed at developing a tool for visual link retrieval and knowledge discovery in painting datasets. The proposed framework is based on a deep convolutional network to perform feature extraction and on a fully-unsupervised nearest neighbor approach to retrieve visual links among digitized paintings. Moreover, the proposed method makes it possible to study influences among artists by means of graph analysis. The tool is intended to help art historians better understand visual arts.

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Notes

  1. 1.

    https://www.wikiart.org.

  2. 2.

    https://www.metmuseum.org/art/collection.

  3. 3.

    https://www.kaggle.com/ikarus777/best-artworks-of-all-time.

  4. 4.

    http://artchallenge.ru.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari, Bari, Italy

    Giovanna Castellano & Gennaro Vessio

Authors
  1. Giovanna Castellano
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  2. Gennaro Vessio
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Corresponding author

Correspondence to Gennaro Vessio .

Editor information

Editors and Affiliations

  1. University of Bari, Bari, Italy

    Michelangelo Ceci

  2. University of Bari, Bari, Italy

    Stefano Ferilli

  3. Sapienza University of Rome, Rome, Italy

    Antonella Poggi

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Castellano, G., Vessio, G. (2020). Towards a Tool for Visual Link Retrieval and Knowledge Discovery in Painting Datasets. In: Ceci, M., Ferilli, S., Poggi, A. (eds) Digital Libraries: The Era of Big Data and Data Science. IRCDL 2020. Communications in Computer and Information Science, vol 1177. Springer, Cham. https://doi.org/10.1007/978-3-030-39905-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-39905-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39904-7

  • Online ISBN: 978-3-030-39905-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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