IDEAS home Printed from https://ideas.repec.org/a/igg/jwsr00/v18y2021i1p1-20.html
   My bibliography  Save this article

A Web Application-Based Secured Image Retrieval System With an IoT-Cloud Network

Author

Listed:
  • Shikha Bhardwaj

    (Deenbandhu Chhotu Ram University of Science and Technology, India & University Institute of Engineering and Technology, Kurukshetra University, Haryana, India)

  • Gitanjali Pandove

    (Deenbandhu Chhotu Ram University of Science and Technology, Haryana, India)

  • Pawan Kumar Dahiya

    (Deenbandhu Chhotu Ram University of Science and Technology, Haryana, India)

Abstract

Many encryption and searching techniques have been used, but they did not prove effective to support smart devices in order to provide input image. Therefore, based on these facts, an effective and novel system has been developed in this paper which is based on content-based search concentrated on encrypted images. Four type of features, namely color moment (CM), Gray level co-occurrence matrix (GLCM), hybrid of CM and GLCM, and lastly, a deep belief network (DBN) has been used here. This deep neural network is based on clustering in combination with indexing and the developed model is called as cluster-based deep belief network (CBDBN) in the present work. A web based application has also been developed using Apache Tomcat server and MATLAB engine. Analysis of many parameters like precision, recall, entropy, correlation coefficient, and time has been done here on benchmark datasets, namely WANG and COIL.

Suggested Citation

  • Shikha Bhardwaj & Gitanjali Pandove & Pawan Kumar Dahiya, 2021. "A Web Application-Based Secured Image Retrieval System With an IoT-Cloud Network," International Journal of Web Services Research (IJWSR), IGI Global, vol. 18(1), pages 1-20, January.
    • Handle: RePEc:igg:jwsr00:v:18:y:2021:i:1:p:1-20
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWSR.2021010101
    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:jwsr00:v:18:y:2021:i:1:p:1-20. 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.