Multimodal Fusion for Video Copy Detection
Content-Based video Copy Detection algorithms (CBCD) focus on detecting video segments that are identical or transformed versions of segments in a known video. In recent years some systems have proposed the combination of orthogonal modalities (e.g., derived from audio and video) to improve detection performance, although not always achieving consistent results. In this paper, the authors propose a fusion algorithm that is able to combine as many modalities as available at the decision level. The algorithm is based on the weighted sum of the normalized scores, which are modified depending on how well they rank in each modality.