IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i7p212-d865398.html
   My bibliography  Save this article

Intuitively Searching for the Rare Colors from Digital Artwork Collections by Text Description: A Case Demonstration of Japanese Ukiyo-e Print Retrieval

Author

Listed:
  • Kangying Li

    (Research Organization of Science and Technology, Ritsumeikan University, Shiga 525-8577, Japan)

  • Jiayun Wang

    (Graduate School of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan)

  • Biligsaikhan Batjargal

    (Research Organization of Science and Technology, Ritsumeikan University, Shiga 525-8577, Japan)

  • Akira Maeda

    (College of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan)

Abstract

In recent years, artworks have been increasingly digitized and built into databases, and such databases have become convenient tools for researchers. Researchers who retrieve artwork are not only researchers of humanities, but also researchers of materials science, physics, art, and so on. It may be difficult for researchers of various fields whose studies focus on the colors of artwork to find the required records in existing databases, that are color-based and only queried by the metadata. Besides, although some image retrieval engines can be used to retrieve artwork by text description, the existing image retrieval systems mainly retrieve the main colors of the images, and rare cases of color use are difficult to find. This makes it difficult for many researchers who focus on toning, colors, or pigments to use search engines for their own needs. To solve the two problems, we propose a cross-modal multi-task fine-tuning method based on CLIP (Contrastive Language-Image Pre-Training), which uses the human sensory characteristics of colors contained in the language space and the geometric characteristics of the sketches of a given artwork in order to gain better representations of that artwork piece. The experimental results show that the proposed retrieval framework is efficient for intuitively searching for rare colors, and that a small amount of data can improve the correspondence between text descriptions and color information.

Suggested Citation

  • Kangying Li & Jiayun Wang & Biligsaikhan Batjargal & Akira Maeda, 2022. "Intuitively Searching for the Rare Colors from Digital Artwork Collections by Text Description: A Case Demonstration of Japanese Ukiyo-e Print Retrieval," Future Internet, MDPI, vol. 14(7), pages 1-21, July.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:7:p:212-:d:865398
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/7/212/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/7/212/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elena Stepanova, 2019. "The impact of color palettes on the prices of paintings," Empirical Economics, Springer, vol. 56(2), pages 755-773, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maksim Borisov & Valeria Kolycheva & Alexander Semenov & Dmitry Grigoriev, 2022. "The influence of color on prices of abstract paintings," Papers 2206.04013, arXiv.org, revised Aug 2022.
    2. Garay, Urbi & Pérez, Eduardo & Pulga, Fredy, 2022. "Color intensity variations and art prices: An examination of Latin American art," Journal of Business Research, Elsevier, vol. 147(C), pages 158-176.
    3. Shailendra Gurjar & Usha Ananthakumar, 2023. "The economics of art: price determinants and returns on investment in Indian paintings," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 50(6), pages 839-859, January.
    4. Stephen Sheppard, 2021. "Image Content, Complexity, and the Market Value of Art," Department of Economics Working Papers 2021-08, Department of Economics, Williams College.
    5. Lan Ju & Zhiyong Tu & Changyong Xue, 2020. "Pricing the Information Quantity in Artworks," Papers 2011.09129, arXiv.org.
    6. Jaehyuk Choi & Lan Ju & Jian Li & Zhiyong Tu, 2023. "Information extraction and artwork pricing," Papers 2302.08167, arXiv.org.
    7. Edward Oczkowski, 2021. "The catalogue raisonné and art auction prices: the case of Berthe Morisot," Empirical Economics, Springer, vol. 61(3), pages 1669-1687, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:7:p:212-:d:865398. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.