Exploiting collective knowledge in an image folksonomy for semantic-based near-duplicate video detection
An increasing number of duplicates and near-duplicates can be found on websites for video sharing. These duplicates and near-duplicates often infringe copyright or clutter search results. Consequently, a high need exists for techniques that allow identifying duplicates and near-duplicates. In this paper, we propose a semantic-based approach towards the task of identifying near-duplicates. Our approach makes use of semantic video signatures that are constructed by detecting semantic concepts along the temporal axis of video sequences. Specifically, we make use of an image folksonomy (i.e., a set of user-contributed images annotated with user-supplied tags) to detect semantic concepts in video sequences, making it possible to exploit an unrestricted concept vocabulary. Comparative experiments using the MUSCLE-VCD-2007 dataset and folksonomy images retrieved from Flickr show that our approach is successful in identifying near-duplicates.