Domain adaptation from RGB-D to RGB images

The introduction of depth cameras offers an opportunity to utilize the depth images to help the object recognition tasks. However, when our target tasks are classifying RGB images, how can we use the RGB-D images? To deal with this problem, we proposed a novel domain adaptation method by learning from RGB-D images in source domain to recognize RGB images in target domain, named DARDR. By introducing the cross modal constraint and the cross domain constraint, our DARDR can maximize the correlations between RGB and depth images in source domain and minimize the domain discrepancy across domains, simultaneously. We incorporate the two terms into the least-squares classifiers. Furthermore, a unified framework is presented to learn the classifier parameters. The advantage of our method is that the correlation between source RGB and depth images and the discrepancy between source and target data can be incorporated with the classifiers of source and target data. To evaluate our DARDR method, we apply it to five cross domain datasets. The experimental results demonstrate that our method can achieve competing performance against the state-of-art methods for object recognition and scene classification tasks. HighlightsWe aim to use RGB-D images to help recognizing RGB images.The correlation between RGB and depth images in source domain is maximized.The cross domain and cross modal constraints are jointly incorporated in the model.A unified framework is presented to learn the classifier parameters.Extensive experiment results show that depth information is useful for DA problems.