| dc.description.abstract | With the explosive growing of Internet, information and knowledge may proliferating wide-spreadly and efficiently. And the computer education is available to all in recent years, let more and more people access varirty material in Internet, But at the same time, it also implyed the flooding of inappropriate Internet content. In the unfortified enviroment, some objectionable topic such as pornography, violence, and hate messages, will penetrate to those who shouldn’t access these web sites. Thus, it is nessessary that apply filting scheme to offensive content, without harmimg to free
speech.
Blacklist is a popular way in current web filtering research, and there are variety collecting method of blacklist, i.e. key word analysis, human inspectnig ...etc.But there are alway some false positive exist. In this paper we develope a compounded method, according to the multiple characteristics of pornography sites in image and text, to refining the blacklist. For erotic images, we use the image processing techniques: color segmentation, coarse detection, median axes extraction, and shape from shading. For text in web document, we use the techniques of Information Retrieval and Document Classification, to measure the number and frequence of erotic key word. After extract two forms of feature vector, we measure the similarity of two document by the angle of their feature vector. Finally, the refining task is cast to the graph partitioning problem, and divide the blacklist into two groups: pornographic site and non-pornographic site. | en_US |