Distance encoded product quantization

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

Volumn , Issue , 2014, Pages 2139-2146

  Heo, Jae Pil ^a   Lin, Zhe ^b   Yoon, Sung Eui ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b ADOBE RESEARCH   (United States)

Author keywords

binary code; image retrieval; Large scale search

Indexed keywords

BINARY CODES; BUDGET CONTROL; COMPUTER VISION; ENCODING (SYMBOLS); IMAGE RETRIEVAL; SIGNAL ENCODING;

CLUSTER CENTERS; DATA POINTS; DISTANCE METRICS; EMBEDDING TECHNIQUE; ENCODING SCHEMES; LARGE-SCALE SEARCH; PRODUCT QUANTIZATIONS; STATE-OF-THE-ART TECHNIQUES;

NEAREST NEIGHBOR SEARCH;

EID: 84911416428     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.274     Document Type: Conference Paper

References (22)

1. A. Banerjee and J. Ghosh. On scaling up balanced clustering algorithms. In SIAM Int. Conf. on Data Mining, 2002.
2. O. Chum, J. Philbin, and A. Zisserman. Near duplicate image detection: min-hash and tf-idf weighting. In BMVC, 2008.
3. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255, june 2009.
4. J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM TOMS, 3(3):209-226, 1977.
5. T. Ge, K. He, Q. Ke, and J. Sun. Optimized product quantization for approximate nearest neighbor search. In CVPR, 2013.
6. Y. Gong and S. Lazebnik. Iterative quantization: a procrustean approach to learning binary codes. In CVPR, 2011.
7. J. He, R. Radhakrishnan, S.-F. Chang, and C. Bauer. Compact hashing with joint optimization of search accuracy and time. In CVPR, 2011.
8. K. He, F. Wen, and J. Sun. K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In CVPR, 2013.
9. J.-P. Heo, Y. Lee, J. He, S.-F. Chang, and S.-E. Yoon. Spherical hashing. In CVPR, 2012.
10. J. Hopcroft. High dimensional data in course notes of mathematical foundations for the information age, 2010.
11. P. Indyk and R. Motwani. Approximate nearest neighbors: toward removing the curse of dimensionality. In STOC, 1998.
12. H. Jégou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large-scale image search. In ECCV, 2008.
13. H. Jégou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. IEEE TPAMI, 2011.
14. B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In ICCV, 2009.
15. Y. Lee, J.-P. Heo, and S.-E. Yoon. Quadra-embedding: Binary code embedding with low quantization error. Computer Vision and Image Understanding (CVIU), 2014. To appear.
16. W. Liu, J. Wang, S. Kumar, and S.-F. Chang. Hashing with graphs. In ICML, 2011.
17. M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. In VISAPP, pages 331-340, 2009.
18. D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In CVPR, 2006.
19. M. Norouzi and D. J. Fleet. Cartesian k-means. In CVPR, 2013.
20. C. Silpa-Anan, R. Hartley, S. Machines, and A. Canberra. Optimised kd-trees for fast image descriptor matching. In CVPR, 2008.
21. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, 2008.
22. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, 2008.