  As important data carriers, the drastically increasing number of multimedia
videos often brings many duplicate and near-duplicate videos in the top results
of search. Near-duplicate video retrieval (NDVR) can cluster and filter out the
redundant contents. In this paper, the proposed NDVR approach extracts the
frame-level video representation based on convolutional neural network (CNN)
features from fully-connected layer and aggregated intermediate convolutional
layers. Unsupervised metric learning is used for similarity measurement and
feature matching. An efficient re-ranking algorithm combined with k-nearest
neighborhood fuses the retrieval results from two levels of features and
further improves the retrieval performance. Extensive experiments on the widely
used CC\_WEB\_VIDEO dataset shows that the proposed approach exhibits superior
performance over the state-of-the-art.
