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Real-time, large-scale duplicate image detection method based on multi-feature fusion

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An Erratum to this article was published on 24 February 2017

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Abstract

Recently, using old or irrelevant images in microblogs to spread false rumors has become increasingly rampant. Therefore, tracking and verifying the sources of images has become essential. In order to solve this problem, this paper provides a real-time, large-scale duplicate image detection method based on multi-feature fusion. This method firstly uses multi-feature fusion to improve retrieval accuracy. Then, by Hbase optimization, it uses a bloom filter and range query to improve retrieval efficiency. Experimental results show that, compared with existing algorithms, this method has higher precision and recall rates. Meanwhile, real-time responsiveness and scalability of the approach also meet real-world needs.

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  • 24 February 2017

    An erratum to this article has been published.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (Grant Nos. U1504608 and 81501548) and the Foundation and Cutting-Edge Technologies Research Program of Henan Province (132300410430).

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Authors and Affiliations

  1. Software Engineering College, Zhengzheng University of Light Industry, Zhengzhou, China

    Ming Chen, Yuhua Li & Zhifeng Zhang

  2. School of Mathematics and Big Data, Foshan University, Foshan, China

    Ching-Hsien Hsu

  3. Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu, Taiwan

    Ching-Hsien Hsu

  4. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

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  1. Ming Chen
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  2. Yuhua Li
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  4. Ching-Hsien Hsu
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Correspondence to Ching-Hsien Hsu.

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Chen, M., Li, Y., Zhang, Z. et al. Real-time, large-scale duplicate image detection method based on multi-feature fusion. J Real-Time Image Proc 13, 557–570 (2017). https://doi.org/10.1007/s11554-016-0632-9

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  • DOI: https://doi.org/10.1007/s11554-016-0632-9

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