Quantize and conquer: A dimensionality-recursive solution to clustering, vector quantization, and image retrieval

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 3024-3031

  Avrithis, Yannis ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

clustering; image retrieval; nearest neighbor search; vector quantization

Indexed keywords

CLUSTERING ALGORITHMS; TREES (MATHEMATICS); VECTOR QUANTIZATION;

CLUSTERING; HIGH DIMENSIONAL SPACES; INDEXING SCHEME; INPUT DATAS; LARGE SCALE EXPERIMENTS; NEAREST NEIGHBOR SEARCH; STATE-OF-THE-ART PERFORMANCE; TREE STRUCTURES;

IMAGE RETRIEVAL;

EID: 84898788849     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.376     Document Type: Conference Paper

References (27)

1. R. Arandjelovic and A. Zisserman. Three things everyone should know to improve object retrieval. In CVPR, 2012.
2. A. Babenko and V. Lempitsky. The inverted multi-index. In CVPR, 2012.
3. T. J. Barth and J. A. Sethian. Numerical schemes for the Hamilton-Jacobi and level set equations on triangulated domains. Journal of Computational Physics, 145(1), 1998.
4. J. Brandt. Transform coding for fast approximate nearest neighbor search in high dimensions. In CVPR, 2010.
5. O. Chum, A. Mikulik, M. Perdoch, and J. Matas. Total recall II: Query expansion revisited. In CVPR, 2011.
6. T. Ge, K. He, Q. Ke, and J. Sun. Optimized product quantization for approximate nearest neighbor search. In CVPR, 2013.
7. D. Greene, M. Parnas, and F. Yao. Multi-index hashing for information retrieval. In FOCS, 1994.
8. S. Hawe, M. Seibert, and M. Kleinsteuber. Separable dictionary learning. Technical report, 2013.
9. H. J́egou, M. Douze, and C. Schmid. On the burstiness of visual elements. In CVPR, 2009.
10. H. J́egou, M. Douze, and C. Schmid. Improving bag-offeatures for large scale image search. IJCV, 87(3), 2010.
11. H. J́egou, M. Douze, and C. Schmid. Exploiting descriptor distances for precise image search. Technical report, 2011.
12. H. J́egou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. PAMI, 33(1), 2011.
13. H. J́egou, R. Tavenard, M. Douze, and L. Amsaleg. Searching in one billion vectors: Re-rank with source coding. In ICASSP, 2011.
14. A. Mikulik, M. Perdoch, O. Chum, and J. Matas. Learning vocabularies over a fine quantization. IJCV, 2012.
15. M. Muja and D. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. In ICCV, 2009.
16. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR, 2006.
17. M. Norouzi and D. Fleet. Cartesian k-means. In CVPR, 2013.
18. M. Norouzi, A. Punjani, and D. J. Fleet. Fast search in Hamming space with multi-index hashing. In CVPR, 2012.
19. M. Perdoch, O. Chum, and J. Matas. Efficient representation of local geometry for large scale object retrieval. In CVPR, 2009.
20. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007.
21. J. Philbin, O. Chum, J. Sivic, M. Isard, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In CVPR, 2008.
22. D. Qin, S. Gammeter, L. Bossard, T. Quack, and L. Van Gool. Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors. In CVPR, 2011.
23. D. Qin, C. Wengert, and L. Van Gool. Query adaptive similarity for large scale object retrieval. In CVPR, 2013.
24. J. Sethian. Fast marching methods. SIAM Review, 41(2), 1999.
25. X. Shen, Z. Lin, J. Brandt, S. Avidan, and Y. Wu. Object retrieval and localization with spatially-constrained similarity measure and k-nn re-ranking. In CVPR, 2012.
26. G. Tolias, Y. Avrithis, and H. J́egou. To aggregate or not to aggregate: Selective match kernels for image search. In ICCV, 2013.
27. P. Turcot and D. Lowe. Better matching with fewer features: the selection of useful features in large database recognition problems. In ICCV, 2009.