A Semi-Supervised Metric Learning for Content-Based Image Retrieval

In this paper, the authors propose a kernel-based approach to improve the retrieval performances 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 that the kernel-based approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric learning methods.