关键词: 最近邻搜索, Markov网络, Laplacian特征映射, 哈希编码

Abstract: Hashing coding is becoming increasingly popular for efficient nearest neighbor search in massive database, due to its high efficiency. However, the exiting hashing methods cannot achieve a satisfied performance with low hash bits because of less original information. To address this problem, this paper proposes a novel method called hashing with Markov random walk graph. Firstly, the method generates sparse representation for high dimensional vectors, then builds the semantic network between the sparse coding by Markov random walk. After that the projection function can be found by Laplacian eigenmap method and the binary hashing coding can be generated by linear projection quickly. Experimental comparisons on two large datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

Key words: nearest neighbor search, Markov network, Laplacian eigenmap, hashing coding