Texture Analysis for Skin Classification in Pornography Content Filtering Based on Support Vector Machine - Nugroho, Hanung Adi; Rahadian, Fauziazzuhry; Adji, Teguh Bharata; Buana, Ratna Lestari Budiani (ITB Journal Publisher, LPPM ITB, 2016)
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.