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Shuo-Fu Yen, Jiann-Jone Chen, Yao-Hong Tsai. Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms[J]. Journal of Electronic Science and Technology, 2017, 15(2): 161-168. DOI: 10.11989/JEST.1674-862X.6062916
Citation: Shuo-Fu Yen, Jiann-Jone Chen, Yao-Hong Tsai. Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms[J]. Journal of Electronic Science and Technology, 2017, 15(2): 161-168. DOI: 10.11989/JEST.1674-862X.6062916
shu

Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms

doi: 10.11989/JEST.1674-862X.6062916
  • 1.

    Electrical Engineering Department,National Taiwan University of Science and Technology,Taipei;

  • 2.

    Information Management Department,Hsuan Chuang University,Hsinchu

Funds: 

This work was supported by MOST under Grant No. 104-2221-E-011-056

More Information
  • Author Bio:

    Shuo-Fu Yen. His research interests include image retrieval, video streaming, and image/video processing;
    Yao-Hong Tsai. His current research interests include image processing, pattern recognition, and computer vision,e-mail: tyh@hcu.edu.tw

    Shuo-Fu Yen. His research interests include image retrieval, video streaming, and image/video processing;
    Yao-Hong Tsai. His current research interests include image processing, pattern recognition, and computer vision,e-mail: tyh@hcu.edu.tw

  • Received Date: 2016-06-28
  • Rev Recd Date: 2016-08-29
  • Publish Date: 2017-06-24
    Abstract
  • With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval (CBIR) scheme is presented. Database-categorizing based on weighted-inverted index (DCWⅡ) and database filtering algorithm (DFA) is used to speed up the features matching process. In the DCWⅡ, the weights are assigned to discrete cosine transform (DCT) coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision (mAP) about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.
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    • References(18)
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