Spam image classification model based on Kolmogorov complexity
基于Kolmogorov复杂性的垃圾图像分类模型*

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..