The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Learning Distance Metrics with Contextual Constraints for Image Retrieval
Abstract
Relevant Component Analysis (RCA) has been proposed for learning distance metrics with contextual constraints for image retrieval. However, RCA has two important disadvantages. One is the lack of exploiting negative constraints which can also be informative, and the other is its incapability of capturing complex nonlinear relationships between data instances with the contextual information. In this paper, we propose two algorithms to overcome these two disadvantages, i.e., Discriminative Component Analysis (DCA) and Kernel DCA. Compared with other complicated methods for distance metric learning, our algorithms are rather simple to understand and very easy to solve. We evaluate the performance of our algorithms on image retrieval in which experimental results show that our algorithms are effective and promising in learning good quality distance metrics for image retrieval.
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Citations
Cites
The information bottleneck method
Tishby, N., Pereira, F. C., & Bialek, W. (2000) · arXiv · doi:10.48550/arxiv.physics/0004057 · Cited by 2,626 · cited 1×
“Tishby et al. [16] considered the joint distribution of two random variables X and Y to be known, and then learned a compact representation of X that enjoys high relevance of Y .”
BackgroundOn a connection between kernel PCA and metric multidimensional scaling
Williams, C. K. I. (2002) · Machine Learning · doi:10.1023/a:1012485807823 · Cited by 206
Cited by
Large scale metric learning from equivalence constraints
Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247939 · Cited by 1,606
Learning a Mahalanobis distance metric for data clustering and classification
Xiang, S., Nie, F., & Zhang, C. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.05.018 · Cited by 621
Natural Language Object Retrieval
Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K., & Darrell, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.493 · Cited by 532
A Survey on Metric Learning for Feature Vectors and Structured Data
Bellet, A., Habrard, A., & Sebban, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.6709 · Cited by 494
Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking
Yu, J., Yang, X., Gao, F., & Tao, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2591583 · Cited by 364
Metadata
- Authors
- Hoi, S. C. H., Liu, W., Lyu, M. R., & Ma, W.-Y.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 2072-2078
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2137736727
- Cited by
- 307
- References
- 2
- FWCI
- 8.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Hoi, S. C. H., Liu, W., Lyu, M. R., & Ma, W.-Y. (2006). Learning Distance Metrics with Contextual Constraints for Image Retrieval. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06). https://doi.org/10.1109/cvpr.2006.167
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