Fast distributed video deduplication via locality-sensitive hashing with similarity ranking

The exponentially growing amount of video data being produced has led to tremendous challenges for video deduplication technology. Nowadays, many different deduplication approaches are being rapidly developed, but they are generally slow and their identification processes are somewhat inaccurate. Till now, there is rare work that studies the generic hash-based distributed framework and the efficient similarity ranking strategy for video deduplication. This paper proposes a flexible and fast distributed video deduplication framework based on hash codes. It is able to support the hash table indexing using any existing hashing algorithm in a distributed environment and can efficiently rank the candidate videos by exploring the similarities among the key frames over multiple tables using MapReduce strategy. Our experiments with a popular large-scale dataset demonstrate that the proposed framework can achieve satisfactory video deduplication performance.