No abstract

No abstract

Porn image detection has been treated as a binary classification task in previous learning based studies. In binary classification methods, the training samples cannot be collected broadly enough, which will limit the generalization ability of the classifiers. This paper seeks to solve this problem with one-class method. First, BoW model mixed with skin color detection is used for feature extraction and image representation. Then we train a one-class visual dictionary from pornographic image training set. Third, to make BoW model suitable for one-class classification, we employ random forests to select important variables and optimize the original BoW vectors. At last, we train a classifier using one-class SVM with unlabeled data (optimized image representation vectors) of pornographic images training set. As far as we are aware, it is the first attempt to treat pornographic image detection as a oneclass classification task. Experimental results on different kinds of testing images demonstrate that the proposed one-class method can achieve a good performance on both normal images and pornographic images with lower time consumption.

No abstract