此处为了方便查找一些基于矢量量化的ANN搜索算法的源代码地址。

Last edit: lzwang, 2020/02/25

AQ

Python Code:

Reference:

  • Babenko A, Lempitsky V. Additive quantization for extreme vector compression[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014: 931-938.

BOPQ

Matlab Code:

Reference:

  • Yu L, Huang Z, Shen F, et al. Bilinear optimized product quantization for scalable visual content analysis[J]. IEEE Transactions on Image Processing, 2017, 26(10): 5057-5069.

C-Kmeans

Matlab Code:

Reference:

  • Norouzi M, Fleet D J. Cartesian k-means[C]//Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. 2013: 3017-3024.

CQ

CPP Code:

Reference:

  • Zhang T, Du C, Wang J. Composite Quantization for Approximate Nearest Neighbor Search[C]//ICML. 2014, 2: 3.
  • Wang J, Zhang T. Composite quantization[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 41(6): 1308-1322.
  • Zhang T, Qi G J, Tang J, et al. Sparse composite quantization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 4548-4556.

LOPQ

Fast-lopq CPP Code:

Yahoo-lopq Matlab Code:

Reference:

  • Kalantidis Y, Avrithis Y. Locally optimized product quantization for approximate nearest neighbor search[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014: 2321-2328.

LSQ

Julia Code:

Python Code:

Reference:

  • Martinez J, Clement J, Hoos H H, et al. Revisiting additive quantization[C]//European Conference on Computer Vision. Springer, Cham, 2016: 137-153.

OPQ

Matlab Code:

Python Code:

Reference:

  • Ge T, He K, Ke Q, et al. Optimized product quantization for approximate nearest neighbor search[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013: 2946-2953.
  • Ge T, He K, Ke Q, et al. Optimized product quantization[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 36(4): 744-755.

PQ _IVFADC

CPP Code:

Matlab Code:

Reference:

  • Jegou H, Douze M, Schmid C. Product quantization for nearest neighbor search[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 33(1): 117-128.

QPQ

Matlab Code:

Reference:

  • An S, Huang Z, Bai S, et al. Quarter-Point Product Quantization for approximate nearest neighbor search[J]. Pattern Recognition Letters, 2019, 125: 187-194.

The Yael Library

Yael is a library implementing computationally intensive functions used in large scale image retrieval, such as neighbor search, clustering and inverted files. The library offers interfaces for C, Python and Matlab.

相关链接:http://yael.gforge.inria.fr