Large Margin Multi-Modal Triplet Metric Learning

Xing Di, Vishal M. Patel

Abstract: Distance metric learning is a significant technique that can improve the similarity accuracy in verification systems. In this paper, we propose a multi-metric learning algorithm with the triplet distance constraints for multi-modal verification problems. The main feature of our algorithm is that when learning multi-metric, we not only enforce the distance between the anchor and the positive samples to be less than the distance between the anchor and the negative samples but we also make the distance between the anchor and the positive samples as small as possible. A simple iterative procedure is introduced to solve the proposed optimization problem. Extensive experiments on three publicly available multi-modal datasets show that our method can perform significantly better than many state-ofthe- art multi-modal metric learning methods.