An unsupervised domain adaptation approach for cross-domain visual classification
For cross-view action recognition and many real-world visual classification problems, one needs to recognize test data at a particular target domain of interest, while training data are collected at a different source domain. Without eliminating such domain differences, recognition of test data using classifiers trained in the source domain will not be expected to produce satisfactory performance. In this paper, we propose a novel domain adaptation approach, which is able to learn a common feature space relating cross-domain data. In particular, we not only aim at matching cross-domain data marginal distributions during adaptation, we also exploit the structure of target domain data and update class-conditional distributions accordingly. Experiments on various cross-domain visual classification tasks would verify the effectiveness and robustness of our proposed method.