A Fast Algorithm for Near-duplicate Video Detection
With the increasing number of multimedia applications in commercial markets,large scale near-duplicate video detection is very desirable for web video processing,especially the computational efficiency is essential for practical applications.In this paper,we present a computationally efficient algorithm based on multi-layer video content analysis.Local features are extracted from key frames of videos and indexed by a novel adaptive locality sensitive hashing scheme.By learning several parameters,fast retrieval in the new hashing structure is performed without high dimensional distance computations and achieves better real-time retrieving performance compared with other state-of-the-art approaches.Then a descriptor filtering method and a two-level matching scheme is performed to generate a relevance score for detection.Experiments on near-duplicate video detection tasks including various transformed videos demonstrate the efficiency gains of the proposed algorithm.