IEEE Trans Pattern Anal Mach Intell. 2018 Sep;40(9):2281-2288. doi: 10.1109/TPAMI.2017.2749576. Epub 2017 Sep 7.
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.
本文提出了一种可共享的个体多视角度量学习（MvML）方法，用于视觉识别。与传统的度量学习方法不同，后者要么在单一类型的特征表示上学习距离度量，要么在多种类型特征的连接表示上学习距离度量，而所提出的 MvML 则联合学习多视角表示上多个距离度量的最优组合，其中，它不仅为每个视图学习了一个单独的距离度量以保留其特定属性，而且还在统一的潜在子空间中为不同视图学习了一个共享表示以保留共同属性。MvML 的目标函数通过成对约束在大间隔学习框架中进行公式化，在该框架下，每对相似样本的距离小于每对不相似样本的距离。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结构来寻求多个非线性变换，将 MvML 扩展到可共享的个体多视角深度度量学习（MvDML）方法中。在人脸验证、亲属验证和人员重识别方面的实验结果表明了所提出的可共享的个体多视角度量学习方法的有效性。
