Complementary hashing for approximate nearest neighbor search

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 1631-1638

  Xu, Hao ^a   Wang, Jingdong ^b   Li, Zhu ^c   Zeng, Gang ^d   Li, Shipeng ^b   Yu, Nenghai ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c Huawei Technology ^*  (United States)

^d Peking University   (China)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE NEAREST NEIGHBOR; APPROXIMATE NEAREST NEIGHBOR SEARCH; HASH TABLE; HASHING METHOD; LOCALITY SENSITIVE HASHING; NEAREST NEIGHBORS; PRECISION AND RECALL;

IMAGE PROCESSING;

IMAGE RETRIEVAL;

EID: 84863036764     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126424     Document Type: Conference Paper

References (22)

1. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS, pages 459-468, 2006.
2. J. L. Bentley. Multidimensional binary search trees used for associative searching. Commun. ACM, 18(9):509-517, 1975.
3. M. Charikar. Similarity estimation techniques from rounding algorithms. In STOC, pages 380-388. ACM, 2002.
4. W. Dong, Z.Wang,W. Josephson, M. Charikar, and K. Li. Modeling LSH for performance tuning. In CIKM, pages 669-678. ACM, 2008.
5. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. The annals of statistics, 28(2):337-407, 2000.
6. J. He, W. Liu, and S. Chang. Scalable similarity search with optimized kernel hashing. In SIGKDD, pages 1129-1138. ACM, 2010.
7. P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In STOC, pages 604-613, 1998.
8. P. Jain, B. Kulis, and K. Grauman. Fast image search for learned metrics. In CVPR, pages 1-8. IEEE, 2008.
9. Y. Jia, J.Wang, G. Zeng, H. Zha, and X.-S. Hua. Optimizing kd-trees for scalable visual descriptor indexing. In CVPR, pages 3392-3399, 2010.
10. B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In ICCV, volume 1, page 3, 2009.
11. W. Liu, J. Wang, S. Kumar, and S.-F. Chang. Hashing with graphs. In ICML, pages 1-8, June 2011.
12. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
13. Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li. Multi-probe lsh: Efficient indexing for high-dimensional similarity search. In VLDB, pages 950-961, 2007.
14. Y. Matsushita and T. Wada. Principal component hashing: An accelerated approximate nearest neighbor search. In PSIVT, pages 374-385, 2009.
15. Y. Mu, J. Shen, and S. Yan. Weakly-supervised hashing in kernel space. In CVPR, pages 3344-3351, 2010.
16. R. Panigrahy. Entropy based nearest neighbor search in high dimensions. In SODA, pages 1186-1195. ACM, 2006.
17. L. Paulevé, H. Jégou, and L. Amsaleg. Locality sensitive hashing: A comparison of hash function types and querying mechanisms. Pat-tern Recognition Letters, 2010.
18. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, pages 1-8, 2008.
19. J.Wang, S. Kumar, and S. Chang. Semi-supervised hashing for scalable image retrieval. In CVPR, pages 3424-3431, 2010.
20. J.Wang, S. Kumar, and S. Chang. Sequential Projection Learning for Hashing with Compact Codes. In ICML, pages 1127-1134, 2010.
21. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, volume 21, pages 1753-1760, 2009.
22. D. Zhang, J. Wang, D. Cai, and J. Lu. Self-taught hashing for fast similarity search. In SIGIR, pages 18-25, 2010.