Abstract
In this article, we develop an integrated adult-content recognition system which can detect adult images, adult videos, and adult Web page bags, where a Web page bag consists of a Web page and a predefined number of Web pages linked to it through hyperlinks. In our adult image-recognition algorithm, we model skin patches rather than skin pixels, resulting in better results than state-of-the-art algorithms which model skin pixels. In our adult video-recognition algorithm, information from the accompanying audio section around an image in an adult video is used to obtain a prior classification of the image. The algorithm achieves a better performance than the ones which use image information alone or audio information alone. The adult Web page bag recognition is carried out using multi-instance learning based on the combination of classifying texts, images and videos in Web pages. Both the speed and the accuracy for recognizing the Web adult content are increased, in contrast to recognizing Web pages one-by-one.
- Andrews, S., Tsochantaridis, I., and Hofmann, T. 2003. Support Vector machines for multiple-instance learning. In Proceedings of the Neural Information Processing Systems Conference. MIT Press, 561--568.Google Scholar
- Aragon, C. R. and Aragon, D. B. 2007. A fast contour descriptor algorithm for supernova image classification. In Proceedings of SPIE Annual Symposium on Electronic Imaging: Real-Time Image Processing. Vol. 6496, 649607.1-649607.12.Google Scholar
- Arentz, W. A. and Olstad, B. 2004. Classifying offensive sites based on image content. Computer Vision Image Understand. 94, 1-3, 295--310. Google Scholar
Digital Library
- Bian, W. and Tao, D. 2011. Max-Min distance analysis by using sequential SDP relaxation for dimension reduction. IEEE Trans. Patt. Anal. Machine Intell. 33, 5, 1037--1050. Google Scholar
Digital Library
- Breiman, L. 2001. Random forests. Machine Learn. 45, 1, 5--32. Google Scholar
Digital Library
- Brown, D., Craw, I., and Lewthwaite, J. 2001. A SOM based approach to skin detection with application in real time systems. In Proceedings of the British Machine Vision Conference. 491--500.Google Scholar
- Buchanan, C. R. 2005. Semantic-based audio recognition and retrieval. Master Thesis, University of Edinburgh.Google Scholar
- Dalal, N. and Triggs, B. 2005. Histograms of oriented gradients for human detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Vol. 1, 886--893. Google Scholar
Digital Library
- Deselaers, T., Pimenidis, L., and Ney, H. 2008. Bag-of-visual-words models for adult image classification and filtering. In Proceedings of International Conference on Pattern Recognition. 1--4.Google Scholar
- Du, R., Safavi-Naini, R., and Susilo, W. 2003. Web filtering using text classification. In Proceedings of the IEEE International Conference on Networks. 325--330.Google Scholar
- Endeshaw, T., Garcia, J., and Jakobsson, A. 2008. Classification of indecent videos by low complexity repetitive motion detection. In Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop. 1--7. Google Scholar
Digital Library
- Felzenszwalb, P. and Huttenlocher, D. 2004. Efficient graph-based image segmentation. Int. J. Computer Vision 59, 2, 167--181. Google Scholar
Digital Library
- Forsyth, D. A. and Fleck, M. M. 1999. Automatic detection of human nudes. International Journal of Computer Vision, 32, 1, 63--77. Google Scholar
Digital Library
- Hammami, M. and Chahir, Y. 2006. WebGuard: A web filtering engine combining textual, structural, and visual content-based analysis. IEEE Trans. Knowl. Data Engin. 18, 2, 272--284. Google Scholar
Digital Library
- Han, S., Jeong, C., and Nam, T. 2005. Multi-Layer objectionable video classification system using local-global information. In Proceedings of the WSEAS International Conference on Computers. 49, 1--5. Google Scholar
Digital Library
- Ho, W. H. and Watters, P. A. 2004. Statistical and structural approaches to filtering internet pornography. In Proceedings of the IEEE International Conference on System, Man and Cybernetics. 5, 4792--4798.Google Scholar
- Hsu, R., Abdel-Mottaleb, M., and Jain, A. K. 2002. Face detection in color images. IEEE Trans. Patt. Anal. Machine Intell. 24, 5, 696--706. Google Scholar
Digital Library
- Hu, W. M., Wu, O., Chen, Z. Y., Fu, Z. Y., and Maybank, S. 2007. Recognition of pornographic web pages by classifying texts and images. IEEE Trans. Patt. Anal. Machine Intell. 29, 6, 1019--1034. Google Scholar
Digital Library
- Ioffe, S. and Forsyth, D. 1999. Finding people by sampling. In Proceedings of the IEEE International Conference on Computer Vision. 1092--1097. Google Scholar
Digital Library
- Ioffe, S. and Forsyth, D. A. 2001. Probabilistic methods for finding people. Inter. J. Computer Vision 43, 1, 45--68. Google Scholar
Digital Library
- Jansohn, C., Ulges, A., and Breuel, T. M. 2009. Detecting pornographic video content by combining image features with motion information. In Proceedings of the ACM International Conference on Multimedia. 601--604. Google Scholar
Digital Library
- Jedynak, B., Zheng, H., Daoudi, M., and Barret, D. 2002. Maximum entropy models for skin detection. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing. 276--281.Google Scholar
- Jiao, F., Gao, W., Duan, L., and Cui, G. 2001. Detecting adult image using multiple features. In Proceedings of the IEEE International Conference on Info-Tech and Info-Net. Vol. 3, 378--383.Google Scholar
- Jones, M. J. and Rehg, J. M. 2002. Statistical color models with application to skin detection. Inter. J. Computer Vision, 46, 1, 81--96. Google Scholar
Digital Library
- Kovac, J., Peer, P., and Solina, F. 2003. Human skin colour clustering for face detection. In Proceedings of the International Conference on Computer as a Tool. Vol. 2, 144--148.Google Scholar
- Lee, J. Y. and Yoo, S. I. 2002. An elliptical boundary model for skin color detection. In Proceedings of the International Conference on Imaging Science, Systems and Technology.Google Scholar
- Lee, P. Y., Hui, S. C., and Fong, A. C. M. 2005. An intelligent categorization engine for bilingual web content filtering. IEEE Trans. Multimedia, 7, 6, 1183--1190. Google Scholar
Digital Library
- Lee, S., Shim, W., and Kim, S. May 2009. Hierarchical system for objectionable video detection. IEEE Trans. Consumer Electron. 55, 2, 677--684. Google Scholar
Digital Library
- Liang, K. M., Scott, S. D., and Waqas, M. 2004. Detecting pornographic images. In Proceedings of the Asian Conference on Computer Vision. 497--502.Google Scholar
- Lienhart, R. and Hauke, R. 2009. Filtering adult image content with topic models. In Proceedings of the IEEE International Conference on Multimedia and Expo. 1472--1475. Google Scholar
Digital Library
- Lienhart, R. and Maydt, J. 2002. An extended set of haar-like features for rapid object detection. In Proceedings of the IEEE International Conference on Image Processing. Vol. 1, I-900-I-903.Google Scholar
- Lopes, A. P. B., Avila1, S. E. F. de., Peixoto, A. N. A., Oliveira1, R. S., and Araujo, A. de A. 2009. A bag-of-features approach based on hue-sift descriptor for nude detection. In Proceedings of the European Signal Processing Conference. 1552--1556.Google Scholar
- Rowley, H. A., Jing, Y. S., and Baluja, S. 2006. Large scale image-based adult-content filtering. In Proceedings of the International Conference on Computer Vision Theory and Applications. Vol. 1, 290--296.Google Scholar
- Sadka, A. H. 2004. Visnet: NoE on networked audiovisual media technologies. In Proceedings of the Workshop on Image Analysis for Multimedia Interactive Services.Google Scholar
- Salton, G. and Buckly, C. 1998. Term weighting approaches in automatic text retrieval. Infor. Process. Manag. 24, 5, 513--523. Google Scholar
Digital Library
- Shih, J.-L., Lee, C.-H., and Yang, C.-S. 2007. An adult image identification system employing image retrieval technique. Patt. Recog. Lett. 28, 16, 2367--2374. Google Scholar
Digital Library
- Tao, D. C., Tang, X. O., Li, X. L., and Wu, X. D. 2006. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Patt. Anal. Machine Intell. 28, 7, 1088--1099. Google Scholar
Digital Library
- Tao, D. C., Li, X. L., Wu, X. D., and Maybank S. J. 2009. Geometric mean for subspace selection. IEEE Trans. Patt. Anal. Machine Intell. 31, 2, 260--274. Google Scholar
Digital Library
- Tax, M. J. and Duin, P. W. 2004. Support vector data description. Machine Learn. 54, 1, 45--66. Google Scholar
Digital Library
- Tuzel, O., Porikli, F., and Meer, P. 2008. Pedestrian detection via classification on riemannian manifolds. IEEE Trans Patt. Anal. Machine Intell. 30, 10, 1713--1727. Google Scholar
Digital Library
- Viola, P. and Jones, M. J. 2001. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Vol. 1, 511--518.Google Scholar
- Wang, J. and Zuchker, J.-D. 2000. Solving the multiple-instance problem: A lazy learning approach. In Proceedings of the International Conference on Machine Learning. 1119--1125. Google Scholar
Digital Library
- Wang, J. Z., Li, J., Wiederhold, G., and Firschein, O. 1998. System for screening objectionable images. Computer Comm. 21, 15, 1355--1360. Google Scholar
Digital Library
- Wang, J. Z., Li, J., Wiederhold, G., and Firschein, O. 1998. Classifying objectionable websites based on image content. In Lecture Notes in Computer Science, Special Issue on Interactive Distributed Multimedia Systems and Telecommunication Services, T. Plagemann and V. Goebel Eds., vol. 1483, 113--124. Google Scholar
Digital Library
- Yang, T. 2006. Applications of computational verbs to effective and realtime image understanding. Int. J. Comput. Cognition, 4, 1, 49--67.Google Scholar
- Yang, Y. 1997. A comparative study on feature selection in text categorization. In Proceedings of the International Conference on Machine Learning. 410--420. Google Scholar
Digital Library
- Zhang, M.-L. and Zhou, Z.-H. 2008. M3MIML: A maximum margin method for multi-instance multi-label learning. In Proceedings of the IEEE International Conference on Data Mining. 688--697. Google Scholar
Digital Library
- Zheng, H., Daoudi, M., and Jedynak, B. 2004. Blocking adult images based on statistical skin detection. Electron. Letters on Computer Vision Image Anal. 4, 2, 1--14.Google Scholar
Cross Ref
- Zhou, Z. H., Jiang, K., and Li, M. 2005. Multi-instance learning based web mining. Appl. Intell. 22, 2, 135--147. Google Scholar
Digital Library
- Zuo, H., Hu, W., and Wu, O. 2010. Patch-based skin color detection and its application to pornography image filtering. In Proceedings of the International Conference on World Wide Web. 1227--1228. Google Scholar
Digital Library
- Zuo, H., Wu, O., Hu, W., and Xu, B. 2008. Recognition of blue movies by fusion of audio and video. In Proceedings of the IEEE International Conference on Multimedia and Expo. 37--40.Google Scholar
Index Terms
Recognition of adult images, videos, and web page bags
Recommendations
Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos
This paper proposes a facial expression recognition (FER) method in videos. The proposed method automatically selects the peak expression face from a video sequence using closeness of the face to the neutral expression. The severely non-frontal faces ...
Recognition of Pornographic Web Pages by Classifying Texts and Images
With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, ...
Recognizing Adult Image Groups for Web Site Classification
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningThe recognition accuracy of adult image groups depends on the performance of the adult image recognizer and the final decision rule. Earlier methods of recognizing adult image groups do not take into account the performance tuning of the adult image ...






Comments