Texture analysis for skin classification in pornography content filtering based on support vector machine

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. © 2016 published by itb journal publisher.