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.
Algorithm design and analysis, Asia, Clustering algorithms, Euclidean distance, Image analysis, Image retrieval, Information retrieval, Kernel, Machine learning algorithms, Shape
In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2006)
HOI, Steven; Liu, Wei; Lyu, Michael R.; and Ma, Wei-Ying.
Learning Distance Metrics with Contextual Constraints for Image Retrieval. (2006). In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2006). Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2392