  With the growing amount of inappropriate content on the Internet, such as
pornography, arises the need to detect and filter such material. The reason for
this is given by the fact that such content is often prohibited in certain
environments (e.g., schools and workplaces) or for certain publics (e.g.,
children). In recent years, many works have been mainly focused on detecting
pornographic images and videos based on visual content, particularly on the
detection of skin color. Although these approaches provide good results, they
generally have the disadvantage of a high false positive rate since not all
images with large areas of skin exposure are necessarily pornographic images,
such as people wearing swimsuits or images related to sports. Local feature
based approaches with Bag-of-Words models (BoW) have been successfully applied
to visual recognition tasks in the context of pornography detection. Even
though existing methods provide promising results, they use local feature
descriptors that require a high computational processing time yielding
high-dimensional vectors. In this work, we propose an approach for pornography
detection based on local binary feature extraction and BossaNova image
representation, a BoW model extension that preserves more richly the visual
information. Moreover, we propose two approaches for video description based on
the combination of mid-level representations namely BossaNova Video Descriptor
(BNVD) and BoW Video Descriptor (BoW-VD). The proposed techniques are
promising, achieving an accuracy of 92.40%, thus reducing the classification
error by 16% over the current state-of-the-art local features approach on the
Pornography dataset.
