Abstract
Recognizing objectionable content draws more and more attention nowadays given the rapid proliferation of images and videos on the Internet. Although there are some investigations about violence video detection and pornographic information filtering, very few existing methods touch on the problem of violence detection in still images. However, given its potential use in violence webpage filtering, online public opinion monitoring and some other aspects, recognizing violence in still images is worth being deeply investigated. To this end, we first establish a new database containing 500 violence images and 1500 non-violence images. And we use the Bag-of-Words (BoW) model which is frequently adopted in image classification domain to discriminate violence images and non-violence images. The effectiveness of four different feature representations are tested within the BoW framework. Finally the baseline results for violence image detection on our newly built database are reported.