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 capt...

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Main Authors: HOI, Steven C. H., LIU, Wei, LYU, Michael R., MA, Wei-Ying
格式: text
語言:English
出版: Institutional Knowledge at Singapore Management University 2006
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在線閱讀:https://ink.library.smu.edu.sg/sis_research/2392
https://ink.library.smu.edu.sg/context/sis_research/article/3392/viewcontent/CVPR06_LiuWei.pdf
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機構: Singapore Management University
語言: English
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總結: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.