This paper presents a sharable and individual multi-view metric learning (MvML) approach for visual recognition. Unlike conventional metric leaning methods which learn a distance metric on either a single type of feature representation or a concatenated representation of multiple types of features, the proposed MvML jointly learns an optimal combination of multiple distance metrics on multi-view representations, where not only it learns an individual distance metric for each view to retain its specific property but also a shared representation for different views in a unified latent subspace to preserve the common properties. The objective function of the MvML is formulated in the large margin learning framework via pairwise constraints, under which the distance of each similar pair is smaller than that of each dissimilar pair by a margin. Moreover, to exploit the nonlinear structure of data points, we extend MvML to a sharable and individual multi-view deep metric learning (MvDML) method by utilizing the neural network architecture to seek multiple nonlinear transformations. Experimental results on face verification, kinship verification, and person re-identification show the effectiveness of the proposed sharable and individual multi-view metric learning methods.

中文翻译：

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可共享的个人多视图度量学习

本文提出了一种可共享的个人多视图度量学习（MvML）的视觉识别方法。与传统的度量学习方法不同，传统的方法学习方法是在单一类型的特征表示或多种类型的特征的级联表示中学习距离度量，而提出的MvML联合学习在多视图表示上的多个距离度量的最佳组合，学习每个视图的单独距离度量以保留其特定属性，还可以在统一的潜在子空间中为不同视图共享视图以保留公共属性。MvML的目标函数是通过成对约束在大余量学习框架中制定的，在该框架下，每个相似对的距离比每个不相似对的距离小一个余量。此外，为了利用数据点的非线性结构，我们通过利用神经网络体系结构寻求多个非线性变换，将MvML扩展为可共享的个人多视图深度度量学习（MvDML）方法。在面部验证，亲属关系验证和人员重新识别方面的实验结果表明，所提出的可共享和个性化的多视图度量学习方法是有效的。