Ordinal margin metric learning and its extension for cross-distribution image data

Propose a novel ORdinal Margin Metric Learning (ORMML) by separating the data with large margins and making them distributed in order.Extend ORMML to cope with cross-distribution application scenarios, named CD-ORMML.Extensively demonstrate the superiority of the proposed metric learning methods to related state-of-the-art methods. In machine learning and computer vision fields, a wide range of applications, such as human age estimation and head pose recognition, are related to ordinal data in which there exists an order relationship. To perform such ordinal estimations in a desired metric space, in this paper we first propose a novel ordinal margin metric learning (ORMML) method by separating the data classes with a sequence of margins, which makes the classes distribute orderly in the learned metric space. Then, to cope with more realistic scenarios where the data are sampled with each class across multiple distributions, we present a cross-distribution variant of ORMML, coined as CD-ORMML, by maximizing the correlation between distributions within each class when conducting metric learning. Finally, extensive experiments on synthetic and publicly available image datasets demonstrate the superiority of the proposed methods in performance to the state-of-the-art methods.