Joint inter-intra representation learning for pornographic video classification

Abstract

This paper addresses video inter-intra similarity retrieval for pornographic classification. The main approaching method is obtaining the internal representation and external similarity between a single unlabeled video and batches of labeled videos, then combining together to determine its label. For the internal representation, we extracted inner features within frames and clustered them to find the representative centroid as the intra-feature. For the external similarity, we utilized a similarity video learning named ViSiL to calculate distance score between two videos using chamfer similarity. With distance scores between input video and batches of pornographic/nonpornographic videos, the inter feature of the input video is obtained. Finally, the inter similarity vector and the intra representation are then concatenated together and fed to a final classifier to identify whether the video is for adults or not. In experiment, our method performs 96.88% accuracy on NPDI-2k, achieved a comparative result comparing to other state-of-the-art methods on the pornographic classification problem.