Fast Nearest Neighbor Search in the Hamming Space

  • Zhansheng JiangEmail author
  • Lingxi Xie
  • Xiaotie Deng
  • Weiwei Xu
  • Jingdong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9516)


Recent years have witnessed growing interests in computing compact binary codes and binary visual descriptors to alleviate the heavy computational costs in large-scale visual research. However, it is still computationally expensive to linearly scan the large-scale databases for nearest neighbor (NN) search. In [15], a new approximate NN search algorithm is presented. With the concept of bridge vectors which correspond to the cluster centers in Product Quantization [10] and the augmented neighborhood graph, it is possible to adopt an extract-on-demand strategy on the online querying stage to search with priority. This paper generalizes the algorithm to the Hamming space with an alternative version of k-means clustering. Despite the simplicity, our approach achieves competitive performance compared to the state-of-the-art methods, i.e., MIH and FLANN, in the aspects of search precision, accessed data volume and average querying time.


Approximate nearest neighbor search Hamming space Bridge vectors Augmented neighborhood graph 



Weiwei Xu is partially supported by NSFC 61322204.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhansheng Jiang
    • 1
    Email author
  • Lingxi Xie
    • 2
  • Xiaotie Deng
    • 1
  • Weiwei Xu
    • 3
  • Jingdong Wang
    • 4
  1. 1.Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Tsinghua UniversityBeijingPeople’s Republic of China
  3. 3.Hangzhou Normal UniversityHangzhouPeople’s Republic of China
  4. 4.Microsoft ResearchBeijingPeople’s Republic of China

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