%0 Journal Article
%T Spam image classification model based on Kolmogorov complexity<br>基于Kolmogorov复杂性的垃圾图像分类模型*
%A DENG Wei
%A CHENG Hong-rong
%A QIAN Wei-zhong
%A QIN Zhi-guang
%A <br>邓蔚
%A 程红蓉
%A 钱伟中
%A 秦志光
%J 计算机应用研究
%D 2011
%I 
%X To further fight against the wide spread of image spam, a spam image classification model based on Kolmogorov complexity is first proposed in this paper. It uses data compression to classify spam images effectively. Compared with current mainstream classification methods for spam image, the model needs neither text extracting from image, nor the feature definition and feature selection of image. It is a kind of parameter-free classification method. And the effectiveness and robustness of the model are verified through the experiments. Also Kolmogorov complexity is indicated to be promising in spam filtering..
%K spam image filtering
%K Kolmogorov complexity
%K data compression
%K machine learning
%K parameter-free classification<br>垃圾图像过滤
%K 柯尔莫哥洛夫复杂性
%K 数据压缩
%K 机器学习
%K 无参数分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=3B6C9221B007B473615FCE12C9FF509E&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=E158A972A605785F&sid=2838795948C22802&eid=CC00A075BF965716&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=16