Advertisement

Appropriate Feature Selection and Post-processing for the Recognition of Artificial Pornographic Images in Social Networks

  • Fangfang Li
  • Siwei Luo
  • Xiyao Liu
  • Jianbin Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10065)

Abstract

Spreading and transmitting pornographic images over the Internet in the form of either real or artificial images is illegal and harmful to teenagers. Because traditional methods are primarily designed to identify real pornographic images, they are less efficient in dealing with artificial images. Therefore, a novel feature selection and post-processing method for the recognition of artificial pornographic images in social networks was proposed in the work. Firstly, features related to image size, skin color region, gray histogram, image color, edge density and direction, Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) were selected. Secondly, a post-processing process for these multiple feature was proposed, which includes two steps. The first step is feature expansion, which is aimed at improving the generalization ability of the recognition model. The other step is rapid feature extraction, which is aimed at reducing the time required for image recognition in social networks. Finally, experimental results demonstrate that the proposed method is effective for the recognition of artificial pornographic images in social networks.

Keywords

Feature selection Image recognition Artificial pornographic image Post-processing Social networks 

Notes

Acknowledgments

This study is supported by the China Postdoctoral Science Foundation (2016M592450), and the Hunan Provincial Natural Science Foundation of China (2016JJ4119).

References

  1. 1.
    Forsyth, D.A., Fleck, M.M.: Identifying nude pictures. In: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, pp. 103–108, Sarasota. IEEE (1996)Google Scholar
  2. 2.
    Fleck, M.M., Forsyth, D.A., Bregler, C.: Finding naked people. In: Buxton, B., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 593–602. Springer, Cambridge (1996). doi: 10.1007/3-540-61123-1_173 Google Scholar
  3. 3.
    Hu, W., Wu, O., Chen, Z., Fu, Z., Maybank, S.: Recognition of pornographic web pages by classifying texts and images. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1019–1034 (2007)CrossRefGoogle Scholar
  4. 4.
    Zhou, L., Zhang, J., Zhao, Y., Zhao, S.: Compressed domain based pornographic image recognition using multi-cost sensitive decision trees. Sig. Process. 93(8), 2126–2139 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, J.Z., Li, J., Wiederhold, G., Firschein, O.: System for screening objectionable images. Comput. Commun. 21(5), 1355–1360 (1998)CrossRefGoogle Scholar
  6. 6.
    Wang, M., Hua, X.S.: Active learning in multimedia annotation and retrieval: a survey. ACM Trans. Intell. Syst. Technol. 2(2), 10–31 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wang, M., Hua, X.S., Tang, J.H., Hong, R.C.: Beyond distance measurement: constructing neighborhood similarity for video annotation. IEEE Trans. Multimed. 11(3), 465–476 (2009)CrossRefGoogle Scholar
  8. 8.
    Wang, M., Hua, X.S., Hong, R.C., Tang, J.H., Qi, G.J., Song, Y.: Unified video annotation via multi-graph learning. IEEE Trans. Circ. Syst. Video Technol. 19(5), 733–746 (2009)CrossRefGoogle Scholar
  9. 9.
    Meng, W.A.N.G., Xian-sheng, H.U.A., Tao, M.E.I., Ri-chang, H.O.N.G., Guo-jun, Q.I., Yan, S.O.N.G., Li-rong, D.A.I.: Semi-supervised kernel density estimation for video annotation. Comput. Vis. Image Underst. 113(3), 384–396 (2009)CrossRefGoogle Scholar
  10. 10.
    Zaidan, A.A., Ahmad, N.N., Karim, H.A., Larbani, M., Zaidan, B.B., Sali, A.: On the multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: an automated anti-pornography system. Neurocomputing 131, 397–418 (2014)CrossRefGoogle Scholar
  11. 11.
    Zheng, H., Daoudi, M., Jedynak, B.: Blocking adult images based on statistical skin detection. Electron. Lett. Comput. Vis. Image Anal. 4(2), 1–14 (2004)Google Scholar
  12. 12.
    Zhang, J., Sui, L., Zhuo, L., Li, Z., Yang, Y.: An approach of bag-of-words based on visual attention model for pornographic images recognition in compressed domain. Neurocomputing 110, 145–152 (2013)CrossRefGoogle Scholar
  13. 13.
    Wang, Y., Li, Y., Gao, W.: Detecting pornographic images with visual words. Trans. Beijing Inst. Technol. 28(5), 410–413 (2008)MathSciNetGoogle Scholar
  14. 14.
    Gao, Y., Wang, M., Zha, Z., Shen, J., Li, X., Wu, X.: Visual-textual joint relevance learning for tag-based social image search. IEEE Trans. Image Process. 22(1), 363–376 (2013)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wang, M., Yang, K., Hua, X., Zhang, H.: Towards a relevant and diverse search of social images. IEEE Trans. Multimed. 12(8), 829–842 (2010)CrossRefGoogle Scholar
  16. 16.
    Meng, W.A.N.G., Bing-bing, N.I., Xian-sheng, H.U.A., Tat-seng, C.H.U.A.: Assistive tagging: a survey of multi-media tagging with human-computer joint exploration. ACM Comput. Surv. 44(4), 1–24 (2012)Google Scholar
  17. 17.
    Dong, K., Guo, L., Fu, Q.: An adult image detection algorithm based on bag-of-visual-words, text information. In: 10th International Conference on Natural Computation, pp. 556–560. IEEE (2014)Google Scholar
  18. 18.
    Liu, Y.Z., Xie, H.T.: Constructing SURF visual-words for pornographic images detection. In: Proceedings of the 12th International Conference on Computers, Information Technology, pp. 404–407. IEEE (2009)Google Scholar
  19. 19.
    Marcial-Basilio, J.A., Aguilar-Torres, G., Snchez-Prez, G., et al.: Detection of pornographic digital images. Int. J. Comput. 2, 298–305 (2010)Google Scholar
  20. 20.
    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
  21. 21.
    Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice-Hall, Upper Saddle River (2011)Google Scholar
  22. 22.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)CrossRefGoogle Scholar
  23. 23.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Information Security and Big Data Research InstituteCentral South UniversityChangshaChina

Personalised recommendations