Advertisement

Combination of SIFT Feature and Convex Region-Based Global Context Feature for Image Copy Detection

  • Zhili ZhouEmail author
  • Xingming Sun
  • Yunlong Wang
  • Zhangjie Fu
  • Yun-Qing Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9023)

Abstract

The conventional content-based image copy detection methods concentrate on finding either global or local features to handle the copy detection task. Unfortunately, the global features are not robust to the cropping attack, while the local features cannot substantially capture context information and thus are not discriminative enough. To address these issues, this paper proposes a novel image copy detection method, which combines both the global and the local features. Firstly, SIFT (scale invariant feature transform) features are extracted and then initially matched between images. Secondly, the SIFT matches are verified by the proposed convex region-based global context (CRGC) features, which describe the global context information around the SIFT features, to effectively remove the false matches. Finally, the number of the surviving SIFT matches is used to determinate whether a test image from image databases is a copy of a given query image. Experimental results have demonstrated the effectiveness of our proposed method in terms of both robustness and discriminability.

Keywords

Image copy detection Copy attacks Convex region Global context information 

Notes

Acknowledgements

This work is supported by the NSFC (61232016, 61173141, 61173142, 61173136, 61103215, 61373132, 61373133), GYHY201206033, 201301030, 2013DFG12860, BC2013012, PAPD fund, Hunan province science and technology plan project fund (2012GK3120), the Scientific Research Fund of Hunan Provincial Education Department (10C0944), and the Prospective Research Project on Future Networks of Jiangsu Future Networks Innovation Institute (BY2013095-4-10)

References

  1. 1.
    Kim, C.: Content-based image copy detection. Sig. Process. Image Commun. 18(3), 169–184 (2003)CrossRefGoogle Scholar
  2. 2.
    Hsiao, J.-H., et al.: A new approach to image copy detection based on extended feature sets. IEEE Trans. Image Process. 16(8), 2069–2079 (2007)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Joly, A., et al.: Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Trans. Multimedia 9(2), 293–305 (2007)CrossRefGoogle Scholar
  4. 4.
    Wu, M.-N., et al.: Novel image copy detection with rotating tolerance. J. Syst. Softw. 80(7), 1057–1069 (2007)CrossRefGoogle Scholar
  5. 5.
    Lin, C.-C., Wang, S.-S.: An edge-based image copy detection scheme. Fundamenta Informaticae 83(3), 299–318 (2008)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Sukthankar. R., Ke, Y., Huston, L.: Efficient near-duplicate detection and subimage retrieval, In: Proceedings of the 12th ACM International Conference on Multimedia (Multimedia), pp. 869−876, New York (2004)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 27 June−2 July 2004, Los Alamitos, pp. 506-513 (2004)Google Scholar
  9. 9.
    Bay, H., et al.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  10. 10.
    Mortensen, E.N., et al.: A SIFT descriptor with global context. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 20−25, June, 2005, San Diego, CA, United states, pp. 184−190 (2005)Google Scholar
  11. 11.
    Xu, Z., et al.: A novel image copy detection scheme based on the local multi-resolution histogram descriptor. Multimedia Tools Appl. 52(2−3), 445–463 (2011)CrossRefGoogle Scholar
  12. 12.
    Ling, H.F., et al.: PM-DFT: a new local invariant descriptor towards image copy detection. J. Comput. Sci. Technol. 26(3), 558–567 (2011)CrossRefzbMATHGoogle Scholar
  13. 13.
    Ling, H., et al.: Efficient image copy detection using multiscale fingerprints. IEEE Multimedia 19, 60–69 (2012)CrossRefGoogle Scholar
  14. 14.
    Mikolajczyk, K., et al.: A comparison of affine region detectors. Int. J. Comput. Vision 65(1−2), 43–72 (2005)CrossRefGoogle Scholar
  15. 15.
    Zhou, W., et al.: Spatial coding for large scale partial-duplicate web image search. In: 18th ACM International Conference on Multimedia ACM Multimedia 2010, MM 2010, 25−29 October 2010, Firenze, Italy, pp. 511−520 (2010)Google Scholar
  16. 16.
    Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Graham, R.L.: An efficient algorithm for determining the convex hull of a finite planar set. Inf. Process. Lett. 1, 132–133 (1972)CrossRefzbMATHGoogle Scholar
  19. 19.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Schmid, C. (ed.) IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE Computer Soc, Los Alamitos (2005)Google Scholar
  20. 20.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhili Zhou
    • 1
    Email author
  • Xingming Sun
    • 1
  • Yunlong Wang
    • 1
  • Zhangjie Fu
    • 1
  • Yun-Qing Shi
    • 1
  1. 1.School of Computer and Software & Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations