Learning to multimodal hash for robust video copy detection

Content-based video copy detection (CBVCD) has attracted increasing attention in recent years. However, video content description and search efficiency are still two challenges in this domain. To cope with these two problems, this paper proposes a novel CBVCD approach with similarity preserving multimodal hash learning (SPM²H). The pre-processed video keyframes are represented as multiple features from different perspectives. SPM²H integrates the multimodal feature fusion and the hashing function learning into a joint framework. Mapping video keyframes into hash codes can conducts fast similarity search in the Hamming space. The experiments show that our approach achieves good performance in accuracy as well as efficiency.