Semi-supervised Online Multi-Task Metric Learning for Visual Recognition and Retrieval

Distance metric learning (DML) is critial in many multimedia application tasks. However, it is hard to learn a satisfactory distance metric given only a few labeled samples for each task. In this paper, we proposed a novel semi-supervised online multi-task DML method termed SOMTML, which enables the models describing different tasks to help each other during the metric learning procedure and thus improving their respective performance. Besides, unlabeled data are leveraged to further help alleviate the data deficiency issue in different tasks by designing a novel regularization term, which also allows some prior information to be incorporated. More importantly, a quite efficient algorithm is developed to update the metrics of all tasks adaptively. The proposed SOMTML is experimentally validated in two popular visual analytic-based applications: handwriting digits recognition and face retrieval. We compared the proposed method with competitive single-task and multi-task metric learning approaches. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed SOMTML.