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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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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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