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A Semi-Supervised Metric Learning for Content-Based Image Retrieval
2011
International Journal of Computer Vision and Image Processing
In this paper, we propose a kernel-based approach to improve the retrieval performance of CBIR systems by learning a distance metric based on class probability distributions. Unlike other metric learning methods which are based on local or global constraints, the proposed method learns for each class a nonlinear kernel which transforms the original feature space to a more effective one. The distances between query and database images are then measured in the new space. Experimental results show
doi:10.4018/ijcvip.2011070104
fatcat:mcdplhdfcfcftpcrrqlqgr4bje