MULTI-VIEW DEEP METRIC LEARNING FOR IMAGE CLASSIFICATION

In this paper, we propose a new deep metric learning approach, called MVDML, for multi-view image classification. Multi-view features can provide more information than single view, however, it is a challenge to exploit and fuse the complementary information from multiple views. Multiple deep neural networks are constructed, each corresponds to a view, to extract nonlinear information from images The nonlinear transformation is an improvement on linear transformation of metric learning. All the original images will be transformed into a lower-dimensional space. In each new space, the difference between intra-class distance and inter-class distance is maximized To extract information from different views as much as possible, the difference between different views of the same image is minimized The numerical experiments verify that our model can obtain competitive performance in image classification and runs faster than the baseline methods.