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Detection of exact and similar partial copies for copyright protection of manga

  • Weihan SunEmail author
  • Koichi Kise
Original Paper

Abstract

Manga, a kind of Japanese comic book, is an important genre in the realm of image publications requiring copyright protection. To copy manga, illegal users generally focus on certain interesting parts from which to make partial copies to apply in their own drawings. With respect to their sources, copying of manga can be divided into two types: (1) exact copies, which duplicate specific contents of manga, such as scanned manga publications (printed copies) and traced outlines of manga (hand-drawn copies), and (2) similar partial copies, which infringe the copyright of manga characters based on their features. In this paper, we propose applying content-based image retrieval methods to detect both exact and similar copies based on two kinds of regions of interest (ROIs): generic ROIs and face ROIs. The method is able not only to locate the partial copies from images with complex backgrounds, but also to report the corresponding copied parts of copyrighted manga pages for exact copy detection and copied manga characters for similar copy detection. The experimental results prove high performance of the proposed method for detecting printed partial copies. In addition, 85 % of hand-drawn and 77 % of similar partial copies were detected with relatively high precision using a database containing more than \(10{,}000\) manga pages.

Keywords

Manga Printed copy Hand-drawn copy Similar copy Partial copy Content-based image retrieval 

Notes

Acknowledgments

This research was supported in part by the Grant-in- Aid for Scientic Research (B)(22300062) from Japan Society for the Promotion of Science (JSPS).

References

  1. 1.
    2007 publication index annual report. The all Japan Magazine and Book publishers’ and Editors Association, the Research Institute for Publications (2002) Google Scholar
  2. 2.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891–923 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bas, P., Chassery, J.M., Macq, B.M.: Geometrically invariant watermarking using feature points. IEEE Trans. Image Process. 11(9), 1014–1028 (2002)CrossRefGoogle Scholar
  4. 4.
    Berrani, S.A., Amsaleg, L., Gros, P.: Robust content-based image searches for copyright protection. In: Proceedings of the 1st ACM International Workshop on Multimedia databases, pp. 70–77 (2003)Google Scholar
  5. 5.
    Berretti, S., Bimbo, A.D., Pala, P.: Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. MultiMedia 2(4), 225–239 (2000)CrossRefGoogle Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition vol. 1, 886–893 (2005)Google Scholar
  7. 7.
    Do, M.N., Vetterli, M.: Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans. Image Process. 11, 146–158 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Douze, M., Jegou, H., Schmid, C.: An image-based approach to video copy detection with spatio-temporal post-filtering. IEEE Trans. Multimedia 12(4), 257–266 (2010)CrossRefGoogle Scholar
  9. 9.
    Eickeler, S., Müller, S.: Content-based video indexing of tv broadcast news using hidden markov models. In: Proceedings of IEEE International Conference on Acoustics, Speech, and, Signal Processing, pp. 2997–3000 (1999)Google Scholar
  10. 10.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)CrossRefGoogle Scholar
  11. 11.
    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)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Fishbein, J.: Europe’s manga mania. BusinessWeek (2007)Google Scholar
  13. 13.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European Conference on Computational Learning Theory, EuroCOLT ’95, pp. 23–37 (1995)Google Scholar
  14. 14.
    Gionis, A., Indyk ,P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of the 25th International Conference on Very Large Data, Bases, pp. 518–529 (1999)Google Scholar
  15. 15.
    Han, J., Ma, K.: Fuzzy color histogram and its use in color image retrieval. IEEE Trans. Image Process. 11(8), 944–952 (2002)CrossRefGoogle Scholar
  16. 16.
    Heikkila, M., Pietikainen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit. 42(3), 425–436 (2009)CrossRefGoogle Scholar
  17. 17.
    Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Hsiao, J.H., Chen, C.S., Chien, L.F., Chen, M.S.: A new approach to image copy detection based on extended feature sets. IEEE Trans. Image Process. 16(8), 2069–2079 (2007)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Jolliffe, I.: Principal Component Analysis. Springer, Berlin (1986)CrossRefGoogle Scholar
  20. 20.
    Joly, A., Frélicot, C., Buisson, O.: Robust content-based video copy identification in a large reference database. In: Proceedings of the 2nd International Conference on Image and Video Retrieval, pp. 414–424 (2003)Google Scholar
  21. 21.
    Joly, A., Frélicot, C., Buisson, O.: Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Trans. Multimedia 9(2), 293–306 (2007)CrossRefGoogle Scholar
  22. 22.
    Ke, Y., Sukthankar R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 506–513 (2004)Google Scholar
  23. 23.
    Ke, Y., Sukthankar R., Huston L., Ke Y., Sukthankar R.: Efficient near-duplicate detection and sub-image retrieval. In: Proceedings of 12th ACM Conference of Multimedia, pp. 869–876 (2004)Google Scholar
  24. 24.
    Kise, K., Noguchi, K., Iwamura, M.: Simple representation and approximate search of feature vectors for large-scale object recognition. In: Proceedings of the 18th British Machine Vision Conference, vol. 1, pp. 182–191 (2007)Google Scholar
  25. 25.
    Lin, C.Y., Wu, M., Member, S., Bloom, J.A., Cox, I.J., Member, S., Miller, M.L., Lui, Y.M.: Rotation, scale, and translation resilient watermarking for images. IEEE Trans. Image Process. 10, 767–782 (2001)CrossRefzbMATHGoogle Scholar
  26. 26.
    Liu, H., Ding, X.: Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 19–25 (2005)Google Scholar
  27. 27.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  28. 28.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)CrossRefGoogle Scholar
  29. 29.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the 18th British Machine Vision Conference, pp. 384–393 (2002)Google Scholar
  30. 30.
    Matsui, T.: The diffusion of foreign cultural products: The case analysis of Japanese comics (manga) market in the U.S. Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Arts and Cultural Policy Studies, working paper 37(1138) (2009)Google Scholar
  31. 31.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)CrossRefGoogle Scholar
  32. 32.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  33. 33.
    Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: Proceedings of VISAPP International Conference on Computer Vision Theory and Applications, pp. 331–340 (2009)Google Scholar
  34. 34.
    Pereira, S., Pun, T.: Robust template matching for affine resistant image watermarks. IEEE Trans. Image Process. 9(6), 1123–1129 (2000)CrossRefGoogle Scholar
  35. 35.
    Rusinol, M., Llados, J.: Logo spotting by a bag-of-words approach for document categorization. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, pp. 111–115 (2009) Google Scholar
  36. 36.
    Salakhutdinov, R., Hinton, G.: Semantic hashing. Int. J. Approx. Reason. 50(7), 969–978 (2009)CrossRefGoogle Scholar
  37. 37.
    Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 530–535 (1997)CrossRefGoogle Scholar
  38. 38.
    Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter sensitive hashing. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 750–757 (2003)Google Scholar
  39. 39.
    Smith, S.: A new class of corner finder. In: Proceedings of the 3rd International Conference of British Machine Vision, pp. 139–148 (1992)Google Scholar
  40. 40.
    Sun, W., Kise, K.: Detecting printed and handwritten partial copies of line drawings embedded in complex backgrounds. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, pp. 341–345 (2009)Google Scholar
  41. 41.
    Sun, W., Kise, K.: Similar partial copy detection of line drawings using a cascade classifier and feature matching. In: Proceedings of the 4th International Conference on Computational Forensics, pp. 121–132 (2010)Google Scholar
  42. 42.
    Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: Proceedings of IEEE International Conference on Computer Vision and, Pattern Recognition, pp. 1–8 (2008)Google Scholar
  43. 43.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  44. 44.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Proceedings of Neural Information Processing Systems, pp. 1753–1760 (2008)Google Scholar
  45. 45.
    Zhang, D.Q., Chang, S.F.: Detecting image near-duplicate by stochastic attributed relational graph matching with learning. In: Proceedings of the 12th ACM International Conference on Multimedia, pp. 877–884 (2004)Google Scholar
  46. 46.
    Zhao, W.L., Ngo, C.W.: Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection. Trans. Image Process. 18(2), 412–423 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and Intelligent Systems, Graduate School of EngineeringOsaka Prefecture UniversitySakai, OsakaJapan

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