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Frontiers of Computer Science

, Volume 14, Issue 2, pp 259–272 | Cite as

Optimized high order product quantization for approximate nearest neighbors search

  • Linhao LiEmail author
  • Qinghua Hu
Research Article
First Online:

Abstract

Product quantization is now considered as an effective approach to solve the approximate nearest neighbor (ANN) search. A collection of derivative algorithms have been developed. However, the current techniques ignore the intrinsic high order structures of data, which usually contain helpful information for improving the computational precision. In this paper, aiming at the complex structure of high order data, we design an optimized technique, called optimized high order product quantization (O-HOPQ) for ANN search. In O-HOPQ, we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition way. As a result, spatial adjacent elements in the high order data space are grouped into the same sub-space. Then, O-HOPQ generates its spatial structured code-book, by optimizing the quantization distortion. Starting from the structured codebook, the global optimum quantizers can be obtained effectively and efficiently. Experimental results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product quantizers. Besides, the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.

Keywords

product quantization high order structured data tensor theory approximate nearest neighbor search 
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Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61732011) and Applied Fundamental Research Program of Qinghai Province (2019-ZJ-7017).

Supplementary material

11704_2018_7049_MOESM1_ESM.pdf (286 kb)
Optimized high order product quantization for approximate nearest neighbors search

References

  1. 1.
    Wang J, Wang J, Yu N, Li S. Order preserving hashing for approximate nearest neighbor search. In: Proceedings of the ACM Conference on Multimedia. 2013, 133–142CrossRefGoogle Scholar
  2. 2.
    Hu D, Zhang G, Yang Y, Jin Z, Cai D, He X. A unified approximate nearest neighbor search scheme by combining data structure and hashing. In: Proceedings of AAAI Conference on Artificial Intelligence. 2013, 681–687Google Scholar
  3. 3.
    Torralba A, Fergus R, Freeman W T. 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 22(1): 1958–1970CrossRefGoogle Scholar
  4. 4.
    Luo W, Qu Z, Pan F, Huang J. A survey of passive technology for digital image forensics. Frontiers of Computer Science, 2007, 1(2): 166–179CrossRefGoogle Scholar
  5. 5.
    Sivic J, Zisserman A. Video google: a text retrieval approach to object matching in videos. In: Proceedings of the IEEE International Conference on Computer Vision. 2003, 1470–1477CrossRefGoogle Scholar
  6. 6.
    Boiman O, Shechtman E, Irani M. In defense of nearest-neighbor based image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2008, 1–8Google Scholar
  7. 7.
    Han B, Zhao X, Tao D, Li X, Hu Z, Hu H. Dayside aurora classification via BIFs-based sparse representation using manifold learning. International Journal of Computer Mathematics, 2014, 91(11): 2415–2426MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Khalili S, Simeone O, Haimovich A. Cloud radio-multistatic radar: joint optimization of code vector and backhaul quantization. IEEE Signal Processing Letters, 2015, 22(4): 494–498CrossRefGoogle Scholar
  9. 9.
    Qin C, Chang C C, Chiu Y P. A novel joint data-hiding and compression scheme based on SMVQ and image inpainting. IEEE Transactions on Image Processing, 2014, 23(3): 969–978MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Ge T, He K, Ke Q, Sun J. Optimized product quantization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(4): 744–755CrossRefGoogle Scholar
  11. 11.
    Brandt J. Transform coding for fast approximate nearest neighbor search in high dimensions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1815–1822Google Scholar
  12. 12.
    Gong Y, Lazebnik S, Gordo A, Perronnin F. Iterative quantization: a Procrustean approach to learning binary codes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2011, 817–824Google Scholar
  13. 13.
    Jegou H, Douze M, Schmid C. Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(1): 117–128CrossRefGoogle Scholar
  14. 14.
    Heo J P, Lin Z, Yoon S. Distance encoded product quantization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 2139–2146Google Scholar
  15. 15.
    Ge T, He K, Ke Q, Sun J. Optimized product quantization for approximate nearest neighbor search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2946–2953Google Scholar
  16. 16.
    Datar M, Immorlica N, Indyk P, Mirrokni V S. Locality sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Symposium on Computational Geometry. 2004, 253–262zbMATHGoogle Scholar
  17. 17.
    Yan C C, Xie H, Zhang B, Ma Y, Dai Q, Liu Y. Fast approximate matching of binary codes with distinctive bits. Frontiers of Computer Science, 2015, 9(5): 741–750CrossRefGoogle Scholar
  18. 18.
    Indyk P, Motwani R. Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of ACM Symposium on Theory of Computing. 1998, 604–613zbMATHGoogle Scholar
  19. 19.
    Weiss Y, Torralba A, Fergus R. Spectral hashing. In: Proceedings of the 22nd Annual Conference on Neural Information Processing Systems. 2009, 1753–1760Google Scholar
  20. 20.
    Kulis B, Darrell T. Learning to hash with binary reconstructive embeddings. In: Proceedings of the 22nd Annual Conference on Neural Information Processing Systems. 2009, 1042–1050Google Scholar
  21. 21.
    Norouzi M, Blei D. Minimal loss hashing for compact binary codes. In: Proceedings of the IEEE International Conference on Machine Learning. 2011, 353–360Google Scholar
  22. 22.
    Liu W, Wang J, Ji R. Supervised hashing with kernels. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 2012, 2074–2081Google Scholar
  23. 23.
    Weiss Y, Fergus R, Torralba A. Multidimensional spectral hashing. In: Proceedings of the IEEE European Conference on Computer Vision. 2012, 304–353Google Scholar
  24. 24.
    He K, Wen F, Sun J. K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2938–2945Google Scholar
  25. 25.
    Li Z, Liu X, Wu J, Su H. Adaptive binary quantization for fast nearest neighbor search. In: Proceedings of the Biennial European Conference on Artificial Intelligence. 2016, 64–72Google Scholar
  26. 26.
    Liu X, Du B, Deng C, Liu M, Lang B. Structure sensitive hashing with adaptive product quantization. IEEE Transactions on Cybernetics, 2016, 46(10): 2252–2264CrossRefGoogle Scholar
  27. 27.
    Wang Z, Feng J, Yan S, Xi H. Linear distance coding for image classification. IEEE Transactions on Image Processing, 2013, 22(2): 537–548MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Sun X, Wang C, Xu C, Zhang L. Indexing billions of images for sketch-based retrieval. In: Proceedings of the ACM Conference on Multimedia. 2013, 233–242CrossRefGoogle Scholar
  29. 29.
    Rusinol M, Aldavert D, Toledo R, Lladôs J. Efficient segmentation-free keyword spotting in historical document collections. Pattern Recognition, 2015, 48(2): 545–555CrossRefGoogle Scholar
  30. 30.
    Jiang Y, Jiang Y, Wang J. VCDB: a large-scale database for partial copy detection in videos. In: Proceedings of the European Conference on Computer Vision. 2014, 357–371Google Scholar
  31. 31.
    Luo J, Zhou W, Wu J. Image categorization with resource constraints: introduction, challenges and advances. Frontiers of Computer Science, 2017, 11(1): 13–26CrossRefGoogle Scholar
  32. 32.
    Revaud J, Douze M, Schmid C, Jégou H. Event retrieval in large video collections with circulant temporal encoding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2459–2466Google Scholar
  33. 33.
    Inoue N, Shinoda K. Neighbor-to-neighbor search for fast coding of feature vectors. In: Proceedings of the IEEE International Conference on Computer Vision. 2013, 1233–1240Google Scholar
  34. 34.
    Norouzi M, Fleet D J. Cartesian k-means. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 3017–3024Google Scholar
  35. 35.
    Kalantidis Y, Avrithis Y. Locally optimized product quantization for approximate nearest neighbor search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 2329–2336Google Scholar
  36. 36.
    Zhang T, Du C, Wang J. Composite quantization for approximate nearest neighbor search. In: Proceedings of the IEEE International Conference on Machine Learning. 2014, 838–846Google Scholar
  37. 37.
    Matsui Y, Yamasaki T, Aizawa K. PQTable: fast exact asymmetric distance neighbor search for product quantization using hash tables. In: Proceedings of the IEEE International Conference on Computer Vision. 2015, 1940–1948Google Scholar
  38. 38.
    Gray R. Vector quantization. IEEE ASSP Magazine, 1984, 1(2): 4–9CrossRefGoogle Scholar
  39. 39.
    Kolda T, Bader B. Tensor decompositions and applications. SIAM Review, 2009, 51(3): 455–500MathSciNetzbMATHCrossRefGoogle Scholar
  40. 40.
    Bader B W, Kolda T G. Algorithm 862: matlab tensor classes for fast algorithm prototyping. ACM Transactions on Mathematical Software, 2006, 32(4): 635–653MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Folland G B. Real Analysis: Modem Techniques and Their Applications. John Wiley and Sons, 2013Google Scholar
  42. 42.
    Hodges D, Danielson D. Nonlinear beam kinematics by decomposition of the rotation tensor. Journal of Applied Mechanics, 1987, 54(2): 258–262zbMATHCrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Artificial IntelligenceHebei University of TechnologyTianjinChina
  2. 2.Tianjin Key Lab of Machine Learning, School of Computer Science and TechnologyTianjin UniversityTianjinChina

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