Abstract
Currently, research on content based image copy detection mainly focuses on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very time-consuming and unscalable. Hence, we need to pay much attention to the efficiency of image detection. In this paper, we propose a fast feature aggregating method for image copy detection which uses machine learning based hashing to achieve fast feature aggregation. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.
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Acknowledgments
Thanks for the funding supported by the National Natural Science Foundation of China (No. 61170135, No. 61202287, No.61440024), and the General Program for Natural Science Foundation of Hubei Province in China(No.2013CFB020, No. 2014CFB590), and Natural Science Foundation of Hubei University of Technology(No. BSQD13039).
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Yan, L., Zou, F., Guo, R. et al. Feature aggregating hashing for image copy detection. World Wide Web 19, 217–229 (2016). https://doi.org/10.1007/s11280-015-0346-0
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DOI: https://doi.org/10.1007/s11280-015-0346-0