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CVPR
2006
IEEE

Learning Distance Metrics with Contextual Constraints for Image Retrieval

9 years 11 months ago
Learning Distance Metrics with Contextual Constraints for Image Retrieval
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.
Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Yin
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Ying Ma
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