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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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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.

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