IDEAS home Printed from https://ideas.repec.org/a/igg/jeco00/v16y2018i1p82-96.html
   My bibliography  Save this article

A Content Based Image Retrieval Method Based on K-Means Clustering Technique

Author

Listed:
  • Mohamed Ouhda

    (Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco)

  • Khalid El Asnaoui

    (Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco)

  • Mohammed Ouanan

    (Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco)

  • Brahim Aksasse

    (Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco)

Abstract

With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.

Suggested Citation

  • Mohamed Ouhda & Khalid El Asnaoui & Mohammed Ouanan & Brahim Aksasse, 2018. "A Content Based Image Retrieval Method Based on K-Means Clustering Technique," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 16(1), pages 82-96, January.
    • Handle: RePEc:igg:jeco00:v:16:y:2018:i:1:p:82-96
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JECO.2018010107
    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:jeco00:v:16:y:2018:i:1:p:82-96. 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.