Texture Analysis for Skin Classification in Pornography Content Filtering Based on Support Vector Machine
DOI:
https://doi.org/10.5614/j.eng.technol.sci.2016.48.5.6Abstract
Nowadays, the Internet is one of the most important things in a human's life. The unlimited access to information has the potential for people to gather any data related to their needs. However, this sophisticated technology also bears a bad side, for instance negative content information. Negative content can come in the form of images that contain pornography. This paper presents the development of a skin classification scheme as part of a negative content filtering system. The data are trained by grey-level co-occurrence matrices (GLCM) texture features and then used to classify skin color by support vector machine (SVM). The tests on skin classification in the skin and non-skin categories achieved an accuracy of 100% and 97.03%, respectively. These results indicate that the proposed scheme has potential to be implemented as part of a negative content filtering system.Downloads
References
The Ministry of Communications and Information Technology, The Rule of the Ministry of Communications and Information Technology (Permenkominfo) of the Republic of Indonesia Number 19, 2014 regarding the Handling of Negative Content on Internet Sites, 2014.
Wang, Y., Li, J., Wang, H. & Hou, Z., Automatic Nipple Detection Using Shape and Statistical Skin Color Information, Advances in Multimedia Modeling, the series Lecture Notes in Computer Science, 5916, pp. 644-649, 2010.
Zuo, H., Hu, W. & Wu, O. Patch-based Skin Color Detection and Its Application to Pornography Image Filtering, in Proceedings of the 19th International Conference on World Wide Web, Raleigh, North Carolina, United States, pp. 1227-1228, 2010.
Santos, C., dos Santos, E.M. & Souto, E., Nudity Detection based on Image Zoning, 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), pp. 1098-1103, 2012.
Nugroho, H.A., Hardiyanto, D. & Adji, T.B., Negative Content Filtering for Video Application, in Proceeding of the 7th International Conference on Information Technology and Electrical Engineering (ICITEE), Chiang Mai, Thailand, 29- 30 October 2015, pp. 55-60, 2015.
Marcial-Basilio, G., Aguilar-Torres, J.A., Sanchez-Perez, G., Toscano-Medina, L.K. & Perez-Meana, H.M., Detection of Pornographic Digital Images, International Journal of Computers, 5(2), pp. 298-305, 2011.
Bouirouga, H., ElFkihi, S., Jilbab, A., & Aboutajdine, D., Skin Detection in Pornographic Videos Using Threshold Technique, Journal of Theoretical and Applied Information Technology, 35(1), pp. 7-19, 2012.
Moustafa, M., Applying Deep Learning to Classify Pornographic Images and Videos, arXiv preprint arXiv:1511.08899, 2015.
Adji, T.B., Rahadian, F., Nugroho, H.A. & Persada, A.G., Negative Content Filtering Based on Skin Texture, Homomorphic Filter and Localizations, in Proceeding of the International Conference on Electrical Engineering and Computer Science (ICEECS), Bali, Indonesia, 24-25 November 2014, pp. 1-6, 2014.
Karavarsamis, S., Ntarmos, N., Blekas, K. & Pitas, I., Detecting Pornographic Images by Localizing Skin ROIs, International Journal of Digital Crime and Forensics (IJDCF), 5(1), pp. 39-53, 2013.
Haralick, R.M. & Shanmugam, K., Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, pp. 610-621, 1973.
Deshmukh, K. & Shinde, G., Adaptive Color Image Segmentation Using Fuzzy Min-Max Clustering, Engineering Letters, 13(2), pp. 57-64, 2006.
Fawcett, T., An Introduction to ROC Analysis, Pattern Recognition Letters, 27(8), pp. 861-874, 2006.
Zhang, H., The Optimality of Naive Bayes, AA, 1(2), p. 3, 2004.
Farag, A.A. & Elhabian, S.Y., A Tutorial on Data Reduction Linear Discriminant Analysis (LDA), University of Louisville, Tech. Rep, 2008.
Kim, K.S., Choi, H.H., Moon, C.S. & Mun, C-W., Comparison of K-Nearest Neighbor, Quadratic Discriminant and Linear Discriminant Analysis in Classification of Electromyogram Signals Based on the Wrist-Motion Directions, Current Applied Physics, 11(3), pp. 740-745, 2011.
Chang, C-C. & Lin, C-J., LIBSVM: a Library for Support Vector Machines, ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), p. 27, 2011.
