A computationally efficient algorithm for large scale near-duplicate video detection

Abstract

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 an 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-ofthe- 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.