Tree quantization for large-scale similarity search and classification

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

Volumn 07-12-June-2015, Issue , 2015, Pages 4240-4248

  Babenko, Artem ^a   Lempitsky, Victor ^b  

^a NATIONAL RESEARCH UNIVERSITY HIGHER SCHOOL OF ECONOMICS   (Russian Federation)

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

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); COMPUTER VISION; DYNAMIC PROGRAMMING; ENCODING (SYMBOLS); ERRORS; IMAGE CLASSIFICATION; INTEGER PROGRAMMING; PATTERN RECOGNITION; TREES (MATHEMATICS);

ENCODING SCHEMES; HIGH-DIMENSIONAL; MULTIPLE CODEBOOKS; PRODUCT QUANTIZATIONS; RETRIEVAL PERFORMANCE; SCALE SIMILARITY; TRAINING DATASET; TREE QUANTIZATION;

VECTOR QUANTIZATION;

EID: 84959212866     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299052     Document Type: Conference Paper

References (19)

1. Gurobi optimizer. http://www. gurobi. com/. 2013
2. A. Babenko and Y. Lempitsky. The inverted multi-index. In CVPR, 2012
3. A. Babenko and Y. Lempitsky. Additive quantization for extreme vector compression. In CVPR, 2014
4. A. Babenko, A. Slesarev, A. Chigorin, and Y. S. Lempitsky. Neural codes for image retrieval. CaRR, absIl404. 1777, 2014
5. A. Bergamo and L. Torresani. Meta-class features for largescale object categorization on a budget. In CVPR, pages 3085-3092, 2012
6. A. Bergamo, L. Torresani, and A. W. Fitzgibbon. Picodes: Learning a compact code for novel-category recognition. In NIPS, pages 2088-2096, 2011
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. pascalnetwork. org/challengeslYOC/voc2007 Iworkshop/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. Jegou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. TPAMI, 33(1), 2011
11. Y. Kalantidis and Y. Avrithis. Locally optimized product quantization for approximate nearest neighbor search. In CVPR. IEEE, 2014
12. J. H. Kappes, B. Andres, F. A. Hamprecht, C. Schnorr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, 1. 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. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages l106-1114, 2012
14. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2), 2004
15. M. Norouzi and D. J. Fleet. Cartesian k-means. In CVPR, 2013
16. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988
17. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. CoRR, absIl403. 6382, 2014
18. J. Sanchez, F. Perronnin, T. Mensink, and J. J. Verbeek. Image classification with the fisher vector: Theory and practice. International Journal of Computer Vision, 105(3): 222-245, 2013
19. T. Zhang, C. Du, and 1. Wang. Composite quantization for approximate nearest neighbor search. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, pages 838-846, 2014.