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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可共享和个人的多视图度量学习


本文提出了一种用于视觉识别的可共享且独立的多视图度量学习（MvML）方法。与在单一类型特征表示或多种类型特征的级联表示上学习距离度量的传统度量学习方法不同，所提出的 MvML 联合学习多视图表示上的多个距离度量的最佳组合，其中不仅学习每个视图的单独距离度量以保留其特定属性，同时学习统一潜在子空间中不同视图的共享表示以保留公共属性。 MvML的目标函数是在大间隔学习框架中通过成对约束制定的，其中每个相似对的距离都比每个不相似对的距离小一定程度。此外，为了利用数据点的非线性结构，我们利用神经网络架构寻求多个非线性变换，将 MvML 扩展为可共享的、单独的多视图深度度量学习（MvDML）方法。人脸验证、亲属关系验证和人员重新识别的实验结果表明了所提出的可共享和个体多视图度量学习方法的有效性。