Fast Video Deduplication via Locality Sensitive Hashing with Similarity Ranking
Yeguang Li, Ke Wen Xia · 2016
The explosive growth of the massive video data brings great challenges to the fast video deduplication. There is encouraging progress of the deduplication techniques in the past few years, especially with the help of the binary hashing methods. However, till now there is rare work that studies the generic hash based framework and the efficient similarity ranking strategy for video deduplication. This paper proposes a flexible and fast video deduplication framework based on hash codes, which supports the hash table indexing using any existing hashing algorithm, and ranks the candidate videos by exploring the similarities among the key frames over multiple tables. Our experiments on the popular large-scale dataset demonstrate that the proposed framework can achieve satisfying performance in the task of video deduplication.