Fast search in Hamming space with multi-index hashing

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 3108-3115

  Norouzi, Mohammad ^a   Punjani, Ali ^a   Fleet, David J ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DISTRIBUTED CODES; FAST SEARCH; HAMMING SPACE; HASH TABLE; IMAGE DATA; K NEAREST NEIGHBOR SEARCH; MULTI-INDEX; RUNTIMES; SUB-STRINGS; VISION APPLICATIONS;

BINARY CODES; DIGITAL STORAGE;

COMPUTER VISION;

EID: 84866714589     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6248043     Document Type: Conference Paper

References (24)

1. M. Aly, M. Munich, and P. Perona. Distributed kd-trees for retrieval from very large image collections. BMVC, 2011.
2. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Comm. ACM, 51:117-122, 2008.
3. M. Charikar. Similarity estimation techniques from rounding algorithms. ACM STOC. 2002.
4. J. Flum and M. Grohe. Parameterized Complexity Theory. Springer, 2006.
5. Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. IEEE CVPR, 2011.
6. D. Greene, M. Parnas, and F. Yao. Multi-index hashing for information retrieval. IEEE FOCS, pp. 722-731, 1994.
7. J. He, R. Radhakrishnan, S.-F. Chang, and C. Bauer. Compact hashing with joint optimization of search accuracy and time. IEEE CVPR, 2011.
8. P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. ACM STOC, pp. 604-613, 1998.
9. H. Jégou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. ECCV, v. I, pp. 304-317, 2008.
10. H. Jégou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. IEEE PAMI, 33:117-128, 2011.
11. H. Jégou, R. Tavenard, M. Douze, and L. Amsaleg. Searching in one billion vectors: re-rank with source coding. IEEE ASSP, pp. 861-864. 2011.
12. B. Kulis and T. Darrell. Learning to hash with binary reconstructive embeddings. NIPS, 2009.
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60:91-110, 2004.
14. M. Minsky and S. Papert. Perceptrons. MIT Press, 1969.
15. M. Muja and D. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. VISSAPP, 2009.
16. M. Norouzi and D. J. Fleet. Minimal Loss Hashing for Compact Binary Codes. ICML, 2011.
17. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42:145-175, 2001.
18. M. Raginsky and S. Lazebnik. Locality-sensitive binary codes from shift-invariant kernels. NIPS, 2009.
19. R. Salakhutdinov and G. Hinton. Semantic hashing. Int. J. Approx. Reasoning, 2009.
20. G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. IEEE ICCV, 2003.
21. C. Strecha, A. Bronstein, M. Bronstein, and P. Fua. Ldahash: improved matching with smaller descriptors. IEEE PAMI, 34:66-78, 2012.
22. A. Torralba, R. Fergus, and W. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE PAMI, 30:1958-1970, 2008.
23. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. IEEE CVPR, 2008.
24. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. NIPS, 2008.