IDEAS home Printed from https://ideas.repec.org/a/igg/jeei00/v5y2015i1p32-46.html
   My bibliography  Save this article

Developing Forecasting Model in Thailand Fashion Market Based on Statistical Analysis and Content-Based Image Retrieval

Author

Listed:
  • Komaek Kawinakrathiti

    (Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University, Bangkok, Thailand)

  • Suphakant Phimoltares

    (Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand)

  • Patcha Utiswannakul

    (Department of Creative Arts, Faculty of Fine and Applied Arts, Chulalongkorn University, Bangkok, Thailand)

Abstract

Traditional trend forecasting process in Thailand fashion industry was challenged by a fast fashion. In this paper, the Content-Based Image Retrieval (CBIR) technique is utilized for retrieval of a fashion trendsetter in fast fashion influence. Firstly, six fashion theories were implemented as 12 variables affecting the trendsetter. Cluster analysis, and factor analysis approach were used to find out the source of a fashion trendsetter as well. Cluster analysis separated all samples into three groups with different fashion ways. Moreover, factor analysis technique grouped all variables into three important factors. From such techniques, Internet media clearly is the best source of a fashion trendsetter. In the authors' model, traditional forecasting sources were added up with a fast fashion influence from CBIR. Then, the CBIR was evaluated in terms of efficiency compared with a real fashion expert in the Thai fashion industry. From statistical test, spatial color distribution yields high efficiency in selecting similar fashion style as a fashion expert.

Suggested Citation

  • Komaek Kawinakrathiti & Suphakant Phimoltares & Patcha Utiswannakul, 2015. "Developing Forecasting Model in Thailand Fashion Market Based on Statistical Analysis and Content-Based Image Retrieval," International Journal of E-Entrepreneurship and Innovation (IJEEI), IGI Global, vol. 5(1), pages 32-46, January.
    • Handle: RePEc:igg:jeei00:v:5:y:2015:i:1:p:32-46
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEEI.2015010103
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jeei00:v:5:y:2015:i:1:p:32-46. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.