Bag-of-visual-words model for artificial pornographic images recognition

Abstract

It is illegal to spread and transmit pornographic images over internet, either in real or in artificial format. The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images. Therefore, criminals turn to release artificial pornographic images in some specific scenes, e.g., in social networks. To efficiently identify artificial pornographic images, a novel bag-of-visual-words based approach is proposed in the work. In the bag-of-words (BoW) framework, speeded-up robust feature (SURF) is adopted for feature extraction at first, then a visual vocabulary is constructed through K-means clustering and images are represented by an improved BoW encoding method, and finally the visual words are fed into a learning machine for training and classification. Different from the traditional BoW method, the proposed method sets a weight on each visual word according to the number of features that each cluster contains. Moreover, a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words. Experimental results indicate that the proposed method outperforms the traditional method.

Keywords

artificial pornographic image / bag-of-words (BoW) / speeded-up robust feature (SURF) descriptors / visual vocabulary

References

[1] ChantrapornchaiC, PromsombatC. Experimental studies on pornographic web filtering techniques. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008KrabiIEEE109-112

[2] ForsythD A, FleckM M. Identifying nude pictures. Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, 1996SarasotaIEEE103-108

[3] FleckM M, ForsythD A, BreglerC. Finding naked people. Proceedings of European Conference on Computer Vision, 1996CambridgeSpringer593-602

[4] HuW, WuO, ChenZ, FuZ, MaybankS. Recognition of pornographic web pages by classifying texts and images [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 1019-1034

[5] ZhuoL, ZhangJ, ZhaoY-d, ZhaoS-wei. Compressed domain based pornographic image recognition using multi-cost sensitive decision trees [J]. Signal Processing, 2013, 93(8): 2126-2139

[6] WangJ Z, LiJ, WiederholdG, FirscheinO. System for screening objectionable images [J]. Computer Communications, 1998, 21(5): 1355-1360

[7] WangM, HuaX S. Active learning in multimedia annotation and retrieval: A survey [J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(2): 10-31

[8] ZhengH, DaoudiM, JedynakB. Blocking adult images based on statistical skin detection [J]. Electronic Letters on Computer Vision and Image Analysis, 2004, 4(2): 1-14

[9] WangM, HuaX S, TangJ H, HongR C. Beyond distance measurement: Constructing neighborhood similarity for video annotation [J]. IEEE Transactions on Multimedia, 2009, 11(3): 465-476

[10] WangM, HuaX S, HongR C, TangJ H, QiG J, SongY. Unified video annotation via multi-graph learning [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19(5): 733-746

[11] WangM, HuaX-s, MeiT, HongR-c, QiG-j, SongY, DaiL-rong. Semi-supervised kernel density estimation for video annotation [J]. Computer Vision and Image Understanding, 2009, 113(3): 384-396

[12] ZaidanA A, AhmadN N, AbdulK H, LarbaniM, ZaidanB B, SaliA. On the multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: An automated anti-pornography system [J]. Neurocomputing, 2014, 131: 397-418

[13] ZhangJ, SuiL, ZhuoL, LiZ-w, YangY-cong. An approach of bag-of-words based on visual attention model for pornographic images recognition in compressed domain [J]. Neurocomputing, 2013, 110: 145-152

[14] WangY-s, LiY-n, GaoWen. Detecting pornographic images with visual words [J]. Transactions of Beijing Institute of Technology, 2008, 28(5): 410-413

[15] GaoY, WangM, ZhaZ-j, ShenJ-l, LiX-l, WuX-dong. Visual-textual joint relevance learning for tag-based social image search [J]. IEEE Transactions on Image Processing, 2013, 22(1): 363-376

[16] WangM, YangK-y, HuaX-s, ZhangH-jiang. Towards a relevant and diverse search of social images [J]. IEEE Transactions on Multimedia, 2010, 12(8): 829-842

[17] WangM, NiB-b, HuaX-s, ChuaT-seng. Assistive tagging: A survey of multi-media tagging with human-computer joint exploration [J]. ACM Computing Surveys, 2012, 44(4): 1-24

[18] UlgesA, StahlA. Automatic detection of child pornography using color visual words. Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, 2011Washington D. CIEEE1-6

[19] SuiL, ZhangJ, ZhuoL, YangY-cong. Research on pornographic images recognition method based on visual words in a compressed domain [J]. IET Image Processing, 2012, 6(1): 87-93

[20] DongK-k, GuoL, FuQ-sheng. An adult image detection algorithm based on bag-of-visual-words and text information. 10th International Conference on Natural Computation, 2014XiamenIEEE556-560

[21] LiuY Z, XieH T. Constructing SURF visual-words for pornographic images detection. Proceedings of the 12th International Conference on Computers and Information Technology, 2009DhakaIEEE404-407

[22] LvL-t, ZhaoC-x, ShangJ, YangY X. Pornographic images filtering model based on high-level semantic bag-of-visual-words [J]. Journal of Computational and Applied Mathematics, 2011, 31(7): 1847-1849

[23] BayH, TuytelaarsT, van GoolL. SURF: Speeded-up robust features. European Conference on Computer Vision, 2006GrazSpringer404-417

[24] LoweD. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(60): 91-110

[25] KeY, SukthankarR. PCA-SIFT: “A More Distinctive Representation for Local Image Descriptors”. Proceedings of Conference on Computer Vision and Pattern Recognition, 2014Washingtion D. C.IEEE511-517

[26] BayH, EssA, TuytelaarsT, van GoolLUC. Speeded-up robust features (SURF) [J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359

[27] CaoJ, ChenH-q, MaoD-h, LiH-s, CaiQiang. Survey of image object recognition based on local features [J]. Journal of Central South University: Science and Technology, 2013, 44(S2): 258-262

[28] CHANG Chih-Chung, LIN Chih-Jen. LIBSVM — A library for support vector machines [EB/OL]. [2015-11-19]. http://www.csie. ntu.edu.tw/~cjlin/libsvm/.