Nude Detection in Video using Bag-of-Visual-Features

The ability to filter improper content from multimedia sources based on visual content has important applications, since text-based filters are clearly insufficient against erroneous and/or malicious associations between text and actual content. In this paper, we investigate a method for detection of nudity in videos based on a bag-of-visual-features representation for frames and an associated voting scheme. Bag-of-Visual-Features (BoVF) approaches have been successfully applied to object recognition and scene classification, showing robustness to occlusion and also to the several kinds of variations that normally curse object detection methods. To the best of our knowledge, only two proposals in the literature use BoVF for nude detection in still images, and no other attempt has been made at applying BoVF for videos. Nevertheless, the results of our experiments show that this approach is indeed able to provide good recognition rates for nudity even at the frame level and with a relatively low sampling ratio. Also, the proposed voting scheme significantly enhances the recognition rates for video segments, achieving, in the best case, a value of 93.2% of correct classification, using a sampling ratio of 1/15 frames. Finally, a visual analysis of some particular cases indicates possible sources of misclassifications.