Fetching the paper…
Reading the bibliography…
Approximate Nearest Neighbor Search (ANNS) in high dimensional space is essential in database and information retrieval.
J. L. Bentley, “Multidimensional binary search trees used for associative searching,” Communications of the ACM , vol. 18, no. 9, pp. 509–517, 1975
1975
Earlier work this paper cites.
K. Fukunaga and P. M. Narendra, “A branch and bound algorithm for computing k-nearest neighbors,” IEEE Transactions on Computers , vol. 100, no. 7, pp. 750–753, 1975
1975
Earlier work this paper cites.
D.-T. Lee and B. J. Schachter, “Two algorithms for constructing a delaunay triangulation,” International Journal of Computer & Information Sciences , vol. 9, no. 3, pp. 219–242, 1980
1980
Earlier work this paper cites.
D. Dearholt, N. Gonzales, and G. Kurup, “Monotonic search networks for computer vision databases,” in Signals, Systems and Computers, 1988. , vol. 2. IEEE, 1988, pp. 548–553
1988
Earlier work this paper cites.
N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, “The R*-tree: an efficient and robust access method for points and rectangles,” in ACM Sigmod Record , vol. 19, no. 2. Acm, 1990, pp. 322–331
1990
Earlier work this paper cites.
F. Aurenhammer, “Voronoi diagrams—a survey of a fundamental geometric data structure,” ACM Computing Surveys (CSUR) , vol. 23, no. 3, pp. 345–405, 1991
1991
Earlier work this paper cites.
J. W. Jaromczyk and G. T. Toussaint, “Relative neighborhood graphs and their relatives,” Proceedings of the IEEE , vol. 80, no. 9, pp. 1502–1517, 1992
1992
Earlier work this paper cites.
S. Arya and D. M. Mount, “Approximate nearest neighbor queries in fixed dimensions,” in SODA , vol. 93, 1993, pp. 271–280
1993
Earlier work this paper cites.
J. S. Beis and D. G. Lowe, “Shape indexing using approximate nearest-neighbour search in high-dimensional spaces,” in 1997 Conference on Computer Vision and Pattern Recognition , 1997, pp. 1000–1006
1997
Earlier work this paper cites.
R. Weber, H.-J. Schek, and S. Blott, “A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces,” in VLDB , vol. 98, 1998, pp. 194–205
1998
Earlier work this paper cites.
A. Gionis, P. Indyk, and R. Motwani, “Similarity search in high dimensions via hashing,” in PVLDB , 1999, pp. 518–529
1999
Earlier work this paper cites.
A. W. Fu, P. M. Chan, Y. L. Cheung, and Y. S. Moon, “Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances,” VLDB Journal , vol. 9, no. 2, pp. 154–173, 2000
2000
Earlier work this paper cites.
J. M. Kleinberg, “Navigation in a small world,” Nature , vol. 406, no. 6798, pp. 845–845, 2000
2000
Earlier work this paper cites.
H. Ferhatosmanoglu, E. Tuncel, D. Agrawal, and A. El Abbadi, “Approximate nearest neighbor searching in multimedia databases,” in Data Engineering, 2001. IEEE, 2001, pp. 503–511
2001
Earlier work this paper cites.
L. Chen, M. T. Özsu, and V. Oria, “Robust and fast similarity search for moving object trajectories,” in Proceedings of the 2005 ACM SIGMOD . ACM, 2005, pp. 491–502
2005
Earlier work this paper cites.
H. V. Jagadish, B. C. Ooi, K. L. Tan, C. Yu, and R. Zhang, “idistance: An adaptive b + -tree based indexing method for nearest neighbor search,” ACM Transactions on Database Systems , vol. 30, no. 2, pp. 364–397, 2005
2005
Cited alongside, same era.
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Object retrieval with large vocabularies and fast spatial matching,” in Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on . IEEE, 2007, pp. 1–8
2007
Cited alongside, same era.
T. Liu, C. R. Rosenberg, and H. A. Rowley, “Clustering billions of images with large scale nearest neighbor search,” in 8th IEEE Workshop on Applications of Computer Vision , 2007, p. 28
2007
Cited alongside, same era.
C. Silpa-Anan and R. Hartley, “Optimised kd-trees for fast image descriptor matching,” in Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition , 2008, pp. 1–8
2008
Cited alongside, same era.
Q. Huang, J. Feng, Y. Zhang, Q. Fang, and W. Ng, “Query-aware locality-sensitive hashing for approximate nearest neighbor search,” PVLDB , vol. 9, no. 1, pp. 1–12, 2015
2015
Later among the works it cites.
Q. Huang, J. Feng, Y. Zhang, Q. Fang, and W. Ng, “Query-aware locality-sensitive hashing for approximate nearest neighbor search,” PVLDB , vol. 9, no. 1, pp. 1–12, 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
Y. Zheng, Q. Guo, A. K. Tung, and S. Wu, “Lazylsh: Approximate nearest neighbor search for multiple distance functions with a single index,” Proceedings of the 2016 International Conference on Management of Data , pp. 2023–2037, 2016
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Weiss, A. Torralba, and R. Fergus, “Spectral hashing,” in Advances in Neural Information Processing Systems , 2009, pp. 1753–1760
2009
Cited alongside, same era.
M. Boguna, D. Krioukov, and K. C. Claffy, “Navigability of complex networks,” Nature Physics , vol. 5, no. 1, pp. 74–80, 2009
2009
Cited alongside, same era.
M. D. Jegou, Herve and C. Schmid, “Product quantization for nearest neighbor search,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 1, pp. 117–128, 2011
2011
Cited alongside, same era.
K. Hajebi, Y. Abbasi-Yadkori, H. Shahbazi, and H. Zhang, “Fast approximate nearest-neighbor search with k-nearest neighbor graph.” in IJCAI 2011 , vol. 22, 2011, pp. 1312–1317
2011
Cited alongside, same era.
W. Dong, C. Moses, and K. Li, “Efficient k-nearest neighbor graph construction for generic similarity measures,” in Proceedings of the 20th International Conference on World Wide Web , 2011, pp. 577–586
2011
Cited alongside, same era.
T. Ge, K. He, Q. Ke, and J. Sun, “Optimized product quantization for approximate nearest neighbor search,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 2946–2953, 2013
2013
Cited alongside, same era.
T. Ge, K. He, Q. Ke, and J. Sun, “Optimized product quantization,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 36, no. 4, pp. 744–755, 2014
2014
Cited alongside, same era.
T. Zhang, C. Du, and J. Wang, “Composite quantization for approximate nearest neighbor search.” in ICML , no. 2, 2014, pp. 838–846
2014
Cited alongside, same era.
X. Liu, C. Deng, B. Lang, D. Tao, and X. Li, “Query-adaptive reciprocal hash tables for nearest neighbor search.” IEEE Transactions on Image Processing , vol. 25, no. 2, pp. 907–919, 2016
2016
Later among the works it cites.
H. Ben and D. Tom, “FANNG: Fast approximate nearest neighbour graphs,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 5713–5722
2016
Later among the works it cites.
2016
Later among the works it cites.
2017
Later among the works it cites.
M. Aumüller, E. Bernhardsson, and A. Faithfull, “Ann-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms,” in International Conference on Similarity Search and Applications . Springer, 2017, pp. 34–49
2017
Later among the works it cites.
A. Arora, S. Sinha, P. Kumar, and A. Bhattacharya, “Hd-index: Pushing the scalability-accuracy boundary for approximate knn search in high-dimensional spaces,” PVLDB , vol. 11, no. 8, pp. 906–919, 2018. [Online]. Available: http://www.vldb.org/pvldb/vol11/p906-arora.pdf
2018
Later among the works it cites.
Y. A. Malkov and D. A. Yashunin, “Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018
2018
Later among the works it cites.
C. Fu, C. Xiang, C. Wang, and D. Cai, “Fast approximate nearest neighbor search with the navigating spreading-out graph,” PVLDB , vol. 12, no. 5, pp. 461–474, 2019
2019
Closest in time.
W. Li, Y. Zhang, Y. Sun, W. Wang, M. Li, W. Zhang, and X. Lin, “Approximate nearest neighbor search on high dimensional data-experiments, analyses, and improvement,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Closest in time.
L. C. Shimomura, R. S. Oyamada, M. R. Vieira, and D. S. Kaster, “A survey on graph-based methods for similarity searches in metric spaces,” Information Systems , p. 101507, 2020
2020
Closest in time.