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

I. Daoudi, K. Idrissi
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
more » ... that our kernel-based approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric learning methods. Index Terms-Similarity search, kernel functions, CBIR, k nearest neighbor search
doi:10.4018/ijcvip.2011070104 fatcat:mcdplhdfcfcftpcrrqlqgr4bje