Appropriate Feature Selection and Post-processing for the Recognition of Artificial Pornographic Images in Social Networks
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.
KeywordsFeature selection Image recognition Artificial pornographic image Post-processing Social networks
This study is supported by the China Postdoctoral Science Foundation (2016M592450), and the Hunan Provincial Natural Science Foundation of China (2016JJ4119).
- 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
- 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
- 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.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.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.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
- 21.Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice-Hall, Upper Saddle River (2011)Google Scholar