Region-based Pornographic Image Detection

Recent advantages in digital images and networks have made pornographic images more accessible than ever. Methods of detecting pornographic images have been proposed in some existing work. However, the skin detection modules adopted are all pixel-based and skin region shape features are rarely properly used. This paper presents a framework for pornographic image detection based on skin region information. Different from traditional works, our approach extracts color and texture features from arbitrary-shaped segmented regions. Then Gaussian mixture models are built for skin and non-skin region classification, and the skin map is produced based on the classification result. Finally, eigenregion features are used to describe the layout of skin regions on the whole image and pornographic images are detected according to the skin modality