IDEAS home Printed from https://ideas.repec.org/a/spr/infott/v26y2024i3d10.1007_s40558-024-00293-0.html
   My bibliography  Save this article

Do deep learning models accurately measure visual destination image? A comparison of a fine-tuned model to past work

Author

Listed:
  • Lyndon J. B. Nixon

    (MODUL University Vienna)

Abstract

The measurement of destination image from visual media such as online photography is of growing significance to destination managers and marketers who want to make better decisions and attract more visitors to their destination. However, there is no single approach with proven accuracy for doing this. We present a new approach where we fine-tune a deep learning model for a predetermined set of cognitive attributes of destination image. We then train state of the art neural networks using labelled tourist photography and test accuracy by comparing results with a ground truth dataset built for the same set of visual classes. Comparing our fine-tuned model against results which follow past approaches, we demonstrate that the pre-trained models without fine-tuning are not as accurate in capturing all of the destination image’s cognitive attributes. This is, to the best of our knowledge, the first deep learning computer vision model trained specifically to measure the cognitive component of destination image from photography and can act as a benchmark for future systems.

Suggested Citation

  • Lyndon J. B. Nixon, 2024. "Do deep learning models accurately measure visual destination image? A comparison of a fine-tuned model to past work," Information Technology & Tourism, Springer, vol. 26(3), pages 377-406, September.
    • Handle: RePEc:spr:infott:v:26:y:2024:i:3:d:10.1007_s40558-024-00293-0
    DOI: 10.1007/s40558-024-00293-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40558-024-00293-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40558-024-00293-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:infott:v:26:y:2024:i:3:d:10.1007_s40558-024-00293-0. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.