  Content-based adult video detection plays an important role in preventing
pornography. However, existing methods usually rely on single modality and
seldom focus on multi-modality semantics representation. Addressing at this
problem, we put forward an approach of analyzing periodicity and saliency for
adult video detection. At first, periodic patterns and salient regions are
respective-ly analyzed in audio-frames and visual-frames. Next, the multi-modal
co-occurrence semantics is described by combining audio periodicity with visual
saliency. Moreover, the performance of our approach is evaluated step by step.
Experimental results show that our approach obviously outper-forms some
state-of-the-art methods.
