Large scale online learning of image similarity through ranking

Journal of Machine Learning Research

Volumn 11, Issue , 2010, Pages 1109-1135

  Chechik, Gal ^a   Sharma, Varun ^b   Shalit, Uri ^c,d   Bengio, Samy ^a  

^a GOOGLE INC   (United States)

^b Google Inc   (Japan)

^c BAR ILAN UNIVERSITY   (Israel)

^d HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Image similarity; Large scale; Metric learning; Online learning

Indexed keywords

DATA SETS; DUAL APPROACH; HUMAN EVALUATION; IMAGE SIMILARITY; LARGE DATASETS; LARGE SCALE DATA; LARGE-SCALE APPLICATIONS; LOSS COSTS; MACHINE-LEARNING; METRIC LEARNING; NEAREST NEIGHBORS; ON-LINE ALGORITHMS; ONLINE LEARNING; ORDER OF MAGNITUDE; SAMPLE SIZES; SCALABLE IMAGE; SEMANTIC SIMILARITY; SIMILARITY FUNCTIONS; SIMILARITY MEASURE; SINGLE CPU; SPARSE REPRESENTATION; STATE-OF-THE-ART METHODS; STORAGE REQUIREMENTS; TEST IMAGES; TEXT QUERY;

E-LEARNING; STATISTICAL TESTS;

LEARNING ALGORITHMS;

EID: 77951165246     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (33)

1. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning distance functions using equivalence relations. In Proc. of 20th International Conference on Machine Learning (ICML), page 11, 2003.
2. L. Bottou. Large-scale machine learning and stochastic algorithms. In NIPS 2008 Workshop on Optimization for Machine Learning, 2008.
3. Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, and L. Cazzanti. Similarity-based classification: Concepts and algorithms. The Journal of Machine Learning Research, 10:747-776, 2009.
4. K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. Online passive-aggressive algorithms. Journal of Machine Learning Research (JMLR), 7:551-585, 2006.
5. J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon. Information-theoretic metric learning. In Proceedings of the 24th international conference on Machine learning, pages 209-216. ACM Press New York, NY, USA, 2007.
6. P. Duygulu, K. Barnard, N. de Freitas, and D. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In European Conference on Computer Vision (ECCV), pages 97-112, 2002.
7. S. L. Feng, R. Manmatha, and V. Lavrenko. Multiple Bernoulli relevance models for image and video annotation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
8. A. Frome, Y. Singer, F. Sha, and J. Malik. Learning globally-consistent local distance functions for shape-based image retrieval and classification. In International Conference on Computer Vision, pages 1-8, 2007.
9. A. Globerson and S. Roweis. Metric learning by collapsing classes. Advances in Neural Information Processing Systems, 18:451, 2006.
10. D. Grangier and S. Bengio. A discriminative kernel-based model to rank images from text queries. Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 30(8):1371-1384, 2008.
11. D. Grangier, F. Monay, and S. Bengio. Learning to retrieve images from text queries with a discriminative model. In International Conference on Adaptive Multimedia Retrieval (AMR), 2006.
12. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007.
13. R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, 2006.
14. P. Jain, B. Kulis, I. Dhillon, and K. Grauman. Online metric learning and fast similarity search. In Advances in Neural Information Processing Systems, volume 22, 2008a.
15. P. Jain, B. Kulis, and K. Grauman. Fast image search for learned metrics. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, 2008b.
16. J.JeonandR.Manmatha.Usingmaximumentropyforautomaticimageannotation. InInternationalConferenceonImageandVideoRetrieval,pages24-32,2004. (Pubitemid39742529)
17. B. Kulis, M. A. Sustik, and I. S. Dhillon. Low-rank kernel learning with bregman matrix divergences. Journal of Machine Learning Research, 10:341-376, 2009.
18. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research (JMLR), 5:27-72, 2004.
19. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV), 60(2):91-110, 2004.
20. W. S. Noble. Multi-kernel learning for biology. In NIPS 2008 workshop on kernel learning, 2008.
21. T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 24(7):971-987, 2002.
22. P. Quelhas, F. Monay, J. M. Odobez, D. Gatica-Perez, T. Tuytelaars, and L. J. Van Gool. Modeling scenes with local descriptors and latent aspects. In International Conference on Computer Vision, pages 883-890, 2005.
23. N. Rasiwasia and N. Vasconcelos. A study of query by semantic example. In 3rd International Workshop on Semantic Learning and Applications in Multimedia, 2008.
24. R. Rosales and G. Fung. Learning sparse metrics via linear programming. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 367-373. ACM New York, NY, USA, 2006.
25. M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. In Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference. Bradford Book, 2004.
26. V. Takala, T. Ahonen, and M. Pietikainen. Block-based methods for image retrieval using local binary patterns. In Scandinavian Conference on Image Analysis (SCIA), 2005.
27. K. Tieu and P. Viola. Boosting image retrieval. International Journal of Computer Vision (IJCV), 56(1):17-36, 2004.
28. A. Torralba, R. Fergus, and W. T. Freeman. Tiny images. Technical Report MIT-CSAIL-TR-2007-2024, Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 2007. URL http://dspace.mit.edu/handle/ 1721.1/37291.
29. A. Tversky. Features of similarity. Psychological Review, 84(4):327-352, 1977.
30. K. Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. Advances in Neural Information Processing Systems, 18:1473, 2006.
31. K. Q. Weinberger and L. K. Saul. Fast solvers and efficient implementations for distance metric learning. In ICML25, pages 1160-1167, 2008.
32. E. P. Xing, A.Y Ng, M. I. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 521-528, Cambridge, MA, 2003. MIT Press.
33. L. Yang. Distance metric learning: A comprehensive survey. Technical report, Michigan State University, 2006.