Multi-View Deep Metric Learning for Volumetric Image Recognition

This paper presents a multi-view deep metric learning (MVDML) architecture for the recognition of volumetric image stacks. Different from existing metric learning methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, the proposed multi-view deep metric learning approach learns a function that maps input volumetric images into a compact Euclidean space where distances approximate the “semantic” distances in the input space. The learning process minimizes a contrastive loss function that drives the similarity metric to be small for pairs of samples from same class, and large for pairs from different classes. The mapping from input to the target space is a multi-view convolutional neural network (MVCNN) which combines information from multiple views of a volumetric image into a single and compact feature descriptor. The experimental results on the nematode volumetric image database show that our proposed method outperforms models based on hand-crafted visual features, conventional metric learning methods and deep classification models.