Nearest neighbor search in Euclidean space is a fundamental problem in multimedia retrieval. The difficulty of exact nearest neighbor search has led to approximate solutions that sacrifice precision for efficiency. Among such solutions, approaches that embed data into binary codes in Hamming space have gained significant success for their efficiency and practical memory requirements. However, binary code searching only finds a big and coarse set of similar neighbors in Hamming space, and hence expensive Euclidean distance based ranking of the coarse set is needed to find nearest neighbors. Therefore, to improve nearest neighbor search efficiency, we proposed a novel binary code method called Integrated Binary Code (IBC) to get a compact set of similar neighbors. Experiments on public datasets show that our method is more efficient and effective than state-of-the-art in approximate nearest neighbor search.
展开▼
