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Feature aggregating hashing for image copy detection

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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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References

  1. Berrani, S. A., Amsaleg, L., Gros, P.: Robust content-based image searches for copyright protection. Int. Workshop Multimed. Databases, 70–77 (2003)

  2. Cai, D., Xiaofei, H., Jiawei, H.: SRDA: An efficient algorithm for large-scale discriminant analysis. IEEE Trans. Knowl. Data Eng. 20(1), 1–12 (2008)

    Article  Google Scholar 

  3. Foo, J.J., Sinha, R.: Pruning sift for scalable near-duplicate image matching. In: Proceedings of the Eighteenth Conference on Australasian Database, pp. 63–71 (2007)

  4. Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: Proceedings of the IEEE Computer Conference on Computer Vision and Pattern Recognition, pp. 817–824 (2011)

  5. Han, Y, Wu, F., Tian, Q., Zhuang, Y.: Image annotation by input-output structural grouping sparsity. IEEE Trans. Image Process. 21(6), 3066–3079 (2012)

    Article  MathSciNet  Google Scholar 

  6. Han, Y., Wu, F., Tao, D., Shao, J., Zhuang, Y., Jiang, J.: Sparse unsupervised dimensionality reduction for multiple view data. IEEE Trans. Circ. Syst. Video Technol. 22(10), 1485–1496 (2012)

    Article  Google Scholar 

  7. Han, Y., Yang, Y., Zhou, X.: Co-regularized ensemble for feature selection. Int. Joint Conf. Artif. Intell., 2013 (2013)

  8. Han, Y., Yang, Y., Yan, Y., Ma, Z., Sebe, N., Zhou, X.: Semi-supervised feature selection via spline regression for video semantic recognition. IEEE Trans. Neural Netw. Learn. Syst. (IEEE T-NNLS) 26(2), 252–264 (2015)

    Article  Google Scholar 

  9. Jegou, H., Perronnin, F., Douze, M., et al.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell., 1704–1716 (2012)

  10. Ke, Y., Sukthankar, R., Huston Larry, L.: Efficient near-duplicate and subimage retrieval. ACM Multimed, 869–876 (2004)

  11. Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2130–2137 (2009)

  12. Kokiopoulou, E., Chen, J., Saad, Y.: Trace optimization and eigenproblems in dimension reduction methods. Numer. Linear Algebra Appl. 18(3), 565–602 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  13. Li, P., Wang, M., Cheng, J., Xu, C., Lu, H.: Spectral hashing with semantically consistent graph for image indexing. IEEE Trans Multimed 15(1), 141–152 (2013)

    Article  Google Scholar 

  14. Poullot, S., Crucianu, M., Buisson, O.: Scalable mining of large video databases using copy detection. ACM Multimed, 61–70 (2008)

  15. Tang, Z., Wang, S., Zhang, X., Wei, W., Su, S.: Robust image hashing for tamper detection using non-negative matrix factorization. J. Ubiquitous Convergence Technol 2(1), 18–26 (2008)

    Google Scholar 

  16. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. Adv. Neural Inf. Process. Syst, 1753–1760 (2009)

  17. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Proceedings of the 2008 Conference Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)

  18. Yan, Y., Liu, G., Wang, S., Zhang, J., Zheng, K.: Graph-based clustering and ranking for diversified image search. Multimed. Systemsation. doi:10.1007/s00530-014-0419-4 (2014)

  19. Yang, Y., Nie, F., Xu, D., Luo, J., Zhuang, Y., Pan, Y.: A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 34(5), 723–742 (2012)

    Article  Google Scholar 

  20. Yang, Y., Ma, Z., Xu, Z., Yan, S., Hauptmann, A.: How related exemplars help complex event detection in web videos? In: Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 1–8 (2013)

  21. Yang, Y., Ma, Z., Nie, F., Chang, X., Hauptmann, A.: Multi-class active learning by uncertainty sampling with diversity maximization. Int. J. Comput. Vis. (IJCV). doi:10.1007/s11263-014-0781-x (2014)

  22. Zhang, D., Wang, J., Cai, D., Lu, J.: Self-taught hashing for fast similarity search. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 18–25 (2010)

  23. Zou, F., Feng, H., Ling, H., Liu, C., Lingyuyan, Li, P., Li, D.: Nonnegative sparse coding induced hashing for image copy detection. Neurocomputing (Elsevier Academic Press) 105(1), 81C89 (2013)

    Google Scholar 

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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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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, China

    Lingyu Yan & Chunzhi Wang

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Fuhao Zou

  3. School of Computer, Southeast University, Nanjing, China

    Rui Guo

  4. School of Computer, University of Electronic Science and Technology of China, Hefei, China

    Lianli Gao

  5. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Ke Zhou

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  1. Lingyu Yan
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  2. Fuhao Zou
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  3. Rui Guo
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  4. Lianli Gao
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  5. Ke Zhou
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  6. Chunzhi Wang
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Correspondence to Rui Guo.

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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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