Towards a better understanding of model-free semantic concept detection for annotation and near-duplicate video clip detection

Given the observation that content transformations tend to preserve semantic information, we demonstrated in previous research that model-free semantic concept detection can be successfully leveraged for identifying NDVCs. In this paper, we seek a better understanding of the usefulness of model-free semantic concept detection for both the task of annotation and NDVC detection. In particular, through extensive experiments, we demonstrate that the problem of detecting semantic concepts for the goal of identifying NDVCs is more relaxed than the problem of detecting semantic concepts for annotation purposes: whereas incorrectly detected semantic concepts negatively affect the effectiveness of annotation, they do not negatively affect the effectiveness of NDVC detection, as long as the same incorrect semantic concepts are detected for both the reference and near-duplicate video clips. This observation has practical implications for the design of a video management system that makes use of model-free semantic concept detection for both the purpose of annotation and NDVC detection.