Towards using semantic features for near-duplicate video detection
An increasing number of near-duplicate video clips (NDVCs) can be found on websites for video sharing. These NDVCs often infringe copyright or clutter search results. Consequently, a high need exists for techniques that allow identifying NDVCs. NDVC detection techniques represent a video clip with a unique set of features. Conventional video signatures typically make use of low-level visual features (e.g., color histograms, local interest points). However, low-level visual features are sensitive to transformations of the video content. In this paper, given the observation that transformations preserve the semantic information in the video content, we study the use of semantic features for the purpose of identifying NDVCs. Experimental results obtained for the MUSCLE-VCD-2007 dataset indicate that semantic features have a high level of robustness against transformations and different keyframe selection strategies. In addition, when relying on the temporal variation of semantic features, semantic video signatures are characterized by a high degree of uniqueness, even when a vocabulary with a low number of semantic concepts is in use (for a query video clip that is sufficiently long).