International Conference on Multimedia Modeling

MultiMedia Modeling pp 325-336 | Cite as

Fast Nearest Neighbor Search in the Hamming Space

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

Abstract

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.

Keywords

Approximate nearest neighbor search Hamming space Bridge vectors Augmented neighborhood graph 

Notes

Acknowledgments

Weiwei Xu is partially supported by NSFC 61322204.

References

  1. 1.
    Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 1958–1970 (2008)CrossRefGoogle Scholar
  2. 2.
    Norouzi, M., Punjani, A., Fleet, D.J.: Fast search in hamming space with multi-index hashing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3108–3115. IEEE (2012)Google Scholar
  3. 3.
    Muja, M., Lowe, D.G.: Fast Matching of Binary Features. In: 9th Conference on Computer and Robot Vision, pp. 404–410. IEEE (2012)Google Scholar
  4. 4.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  5. 5.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision, pp. 2548–2555. IEEE (2011)Google Scholar
  6. 6.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, pp. 2564–2571. IEEE (2011)Google Scholar
  7. 7.
    Norouzi, M., Blei, D.M.: Minimal loss hashing for compact binary codes. In: Proceedings of the 28th International Conference on Machine Learning, pp. 353–360 (2011)Google Scholar
  8. 8.
    Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: Proceedings of the Thirty-Fourth Annual ACM Symposium on Theory of Computing, pp. 380–388. ACM (2002)Google Scholar
  9. 9.
    Babenko, A., Lempitsky, V.: The inverted multi-index. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3069–3076. IEEE (2012)Google Scholar
  10. 10.
    Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117–128 (2011)CrossRefGoogle Scholar
  11. 11.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42, 145–175 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Wang, J., Wang, J., Zeng, G., Tu, Z., Gan, R., Li, S.: Scalable k-NN graph construction for visual descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1106–1113. IEEE (2012)Google Scholar
  13. 13.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18, 509–517 (1975)MATHCrossRefGoogle Scholar
  14. 14.
    Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2161–2168. IEEE (2006)Google Scholar
  15. 15.
    Wang, J., Wang, J., Zeng, G., Gan, R., Li, S., Guo, B.: Fast neighborhood graph search using cartesian concatenation. In: IEEE International Conference on Computer Vision, pp. 2128–2135. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhansheng Jiang
    • 1
  • 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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