Stochastic Multi-view Hashing for Large-scale Near-duplicate Video Retrieval

Near-duplicate video retrieval (NDVR) has been a
significant research task in multimedia given its high impact
in applications, such as video search, recommendation and
copyright protection, etc. In addition to accurate retrieval performance,
the exponential growth of online videos has imposed heavy
demands on the efficiency and scalability of the existing systems.
Aiming at improving both the retrieval accuracy and speed,
we propose a novel stochastic multi-view hashing algorithm to
facilitate the construction of a large-scale NDVR system. Reliable
mapping functions, which convert multiple types of keyframe features,
enhanced by auxiliary information such as video-keyframe
association and ground truth relevance to binary hash code
strings, are learned by maximizing a mixture of the generalized
retrieval precision and recall scores. A composite Kullback-
Leibler (KL) divergence measure is used to approximate the
retrieval scores, which aligns stochastically the neighborhood
structures between the original feature and the relaxed hash code
spaces. The efficiency and effectiveness of the proposed method
are examined using two public near-duplicate video collections,
and are compared against various classical and state-of-the-art
NDVR systems.