Constructing SURF visual-words for pornographic images detection

Pornographic images detection is necessary for us to filter out objectionable information on the Internet. Bag-of-visual-words (BoVW) based pornographic images detection is promising because it can compensate the defect of the traditional approach. However, there are many choices to construct visual-words which are crucial to the tradeoff between the speed and the performance. We propose a novel method of constructing SURF (speeded up robust features) visual-words in skin regions and combining it with color moments. The results show that the performance of SURF visual-words is better than that of SIFT (scale-invariant feature transform) visual-words and our method is more effective to detect pornographic images than many existing methods.