IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v16y2014i1d10.1007_s10796-013-9467-0.html
   My bibliography  Save this article

A scalable approach for content based image retrieval in cloud datacenter

Author

Listed:
  • Jianxin Liao

    (Beijing University of Posts and Telecommunications)

  • Di Yang

    (Beijing University of Posts and Telecommunications)

  • Tonghong Li

    (Technical University of Madrid)

  • Jingyu Wang

    (Beijing University of Posts and Telecommunications)

  • Qi Qi

    (Beijing University of Posts and Telecommunications)

  • Xiaomin Zhu

    (Beijing University of Posts and Telecommunications)

Abstract

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops.

Suggested Citation

  • Jianxin Liao & Di Yang & Tonghong Li & Jingyu Wang & Qi Qi & Xiaomin Zhu, 2014. "A scalable approach for content based image retrieval in cloud datacenter," Information Systems Frontiers, Springer, vol. 16(1), pages 129-141, March.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:1:d:10.1007_s10796-013-9467-0
    DOI: 10.1007/s10796-013-9467-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-013-9467-0
    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/s10796-013-9467-0?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    2. Mohammed Hawa & Raed Al-Zubi & Khalid A. Darabkh & Ghazi Al-Sukkar, 0. "Adaptive approach to restraining content pollution in peer-to-peer networks," Information Systems Frontiers, Springer, vol. 0, pages 1-18.
    3. Mohammed Hawa & Raed Al-Zubi & Khalid A. Darabkh & Ghazi Al-Sukkar, 2017. "Adaptive approach to restraining content pollution in peer-to-peer networks," Information Systems Frontiers, Springer, vol. 19(6), pages 1373-1390, December.
    4. Sanjaya K. Panda & Indrajeet Gupta & Prasanta K. Jana, 0. "Task scheduling algorithms for multi-cloud systems: allocation-aware approach," Information Systems Frontiers, Springer, vol. 0, pages 1-19.
    5. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2019. "Technology in the 21st century: New challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 321-335.
    6. Mengyue Wang & Xin Li & Patrick Y. K. Chau, 2021. "Leveraging Image-Processing Techniques for Empirical Research: Feasibility and Reliability in Online Shopping Context," Information Systems Frontiers, Springer, vol. 23(3), pages 607-626, June.
    7. Ching-Hsien Hsu & Jianhua Ma & Mohammad S. Obaidat, 2014. "Dynamic intelligence towards merging cloud and communication services," Information Systems Frontiers, Springer, vol. 16(1), pages 1-5, March.
    8. Sanjaya K. Panda & Indrajeet Gupta & Prasanta K. Jana, 2019. "Task scheduling algorithms for multi-cloud systems: allocation-aware approach," Information Systems Frontiers, Springer, vol. 21(2), pages 241-259, April.

    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:infosf:v:16:y:2014:i:1:d:10.1007_s10796-013-9467-0. 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.