Additive quantization for extreme vector compression

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

Volumn , Issue , 2014, Pages 931-938

  Babenko, Artem ^a   Lempitsky, Victor ^b  

^a YANDEX   (Russian Federation)

^b SKOLKOVO INSTITUTE OF SCIENCE AND TECHNOLOGY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CODING ERRORS; IMAGE CODING; LEARNING ALGORITHMS; NEAREST NEIGHBOR SEARCH; PATTERN RECOGNITION; VECTORS;

APPROXIMATION ERRORS; CODE-WORDS; COMPRESSION APPROACH; COMPRESSION SCHEME; DESCRIPTORS; HIGH-DIMENSIONAL; PRODUCT QUANTIZATIONS; SCALAR PRODUCT;

VECTOR QUANTIZATION;

EID: 84911413714     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.124     Document Type: Conference Paper

References (23)

1. Gurobi optimizer. http://www.gurobi.com/, 2013.
2. R. Arandjelović and A. Zisserman. Multiple queries for large scale specific object retrieval. In British Machine Vision Conference, 2012.
3. R. Arandjelović and A. Zisserman. Extremely low bit-rate nearest neighbor search using a Set Compression Tree. Technical report, Department of Engineering Science, University of Oxford, 2013.
4. A. Bergamo and L. Torresani. Meta-class features for large-scale object categorization on a budget. In CVPR, pages 3085-3092, 2012.
5. A. Bergamo, L. Torresani, and A. W. Fitzgibbon. Picodes: Learning a compact code for novel-category recognition. In NIPS, pages 2088-2096, 2011.
6. J. Besag. On the Statistical Analysis of Dirty Pictures. J. Roy. Stat. Soc. B, 1986.
7. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html.
8. T. Ge, K. He, Q. Ke, and J. Sun. Optimized product quantization for approximate nearest neighbor search. In CVPR, 2013.
9. Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In CVPR, 2011.
10. H. Jégou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. TPAMI, 33(1), 2011.
11. H. Jegou, M. Douze, C. Schmid, and P. Pérez. Aggregating local descriptors into a compact image representation. In CVPR, 2010.
12. J. H. Kappes, B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, J. Lellmann, N. Komodakis, and C. Rother. A comparative study of modern inference techniques for discrete energy minimization problems. In CVPR, pages 1328-1335, 2013.
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2), 2004.
14. S. Mallat and Z. Zhang. Matching pursuit in a time-frequency dictionary. IEEETransactions on Signal Processing, 41(12):33973415, 1993.
15. M. Norouzi and D. J. Fleet. Cartesian k-means. In CVPR, 2013.
16. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42(3), 2001.
17. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
18. F. Perronnin, J. Sánchez, and T. Mensink. Improving the fisher kernel for large-scale image classification. In ECCV, 2010.
19. J. Sánchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. In CVPR, pages 1665-1672, 2011.
20. S. C. Shapiro. Encyclopedia of Artificial Intelligence. 1987.
21. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, 2008.
22. A. Vedaldi and A. Zisserman. Sparse kernel approximations for efficient classification and detection. In CVPR, pages 2320-2327, 2012.
23. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, pages 1753-1760, 2008.