A Classification of Internet Pornographic Images

Abstract

According to Pornography Statistics,more than 34 percent of Internet users exposeto pornography. There are 12 percent of the total number of websites and 72 million monthly visitors.Internet pornography (Internet Porn) is addictive to teenagers and kids around the world. The normal practice is to block those websites or filter out pornographyfrom kids.In order to do so, researchers has to find a way to detect and classify first. The pixel features including YCbCr range, area of human skin are chosen as pornographyfeatures because of their easy acquisition. C4.5 (Data mining technique)is applied to construct a decision tree. The purpose of this paper is to classify pornography images in a simple if-then rule. The accuracy of experimental result is 85.2%.