A pornographic web page detecting method based on SVM model using text and image features. (2nd August 2006) - Chen, Rung-Ching; Ho, Chun-Te
In this paper, a new web page filtering method directed at pornographic material and based on image and text content analysis is proposed. First, the features of image and text are extracted from the web page content. An analysis is performed and the results are merged. The page is then rated pornographic or non-pornographic. To facilitate real-time analysis, we also propose an acceleration method. In experimental results, text classification accuracy is 95.8% and image classification accuracy is 84%. In addition, the accuracy of web page classification after merging analysis of text and image features is 91.8%.